Information analysis device, information analysis method, and information analysis program

Abstract

To provide an information analysis device, an information analysis method, an information analysis program, and a learning model generation method.SOLUTION: An information analysis device 1 of an embodiment receives answer information which is an answer to a questionnaire from a respondent to perform analysis, and generates an analysis result for each respondent. The information analysis device 1 displays the analysis result in a respondent terminal of a person in charge of personnel, a respondent terminal of a manager, and a respondent terminal of the respondent. The information analysis device 1 displays, as an analysis result of the questionnaire, what each respondent is willing to contribute to the company. According to the present embodiment, from the analysis result analyzed by the information analysis device 1, the person in charge of personnel, the manager, and the respondent can grasp what the respondent is willing to contribute to in the company.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an information analysis device, an information analysis method, an information analysis program, and a learning model generation method, which notify, for example, a respondent of his / her willingness to contribute to an organization. [Background technology]

[0002] In recent years, analysis devices that can analyze the status of employees from the responses of questionnaires administered to members of an organization have become widespread, and are now being implemented by companies and other organizations. For example, there are known organizational development support systems, methods, and programs that obtain scores on expectations and satisfaction from the responses of members to multiple survey questions, enabling on-site and human resources personnel to strengthen and develop the organization themselves (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2018-18152 A Summary of the Invention [Problem to be solved by the invention]

[0004] In an organization such as a company, in order for employees to work independently and voluntarily, it is necessary to continuously diagnose and understand whether employees' motivation to contribute is directed toward the company. However, the technology described in Patent Document 1 visualizes motivation by measuring employees' expectations and satisfaction with the company, so there is a problem in that it is not possible to diagnose what employees' motivation to contribute is directed toward.

[0005] Expectations are a quantification of what employees expect from a company, but what employees expect does not necessarily match their motivation to contribute to the company. In other words, there can be a discrepancy between what people perceive as themselves and what they actually feel, and even if an employee's expectations are met, it does not necessarily mean that they will be motivated to contribute to the company.

[0006] In addition, employee satisfaction is a quantification of how satisfied employees are with a company, and is mainly evaluated based on salary, benefits, work environment, and interpersonal relationships. However, the state in which employees are satisfied does not necessarily coincide with the state in which they are proactively and voluntarily incorporating their work into their work. In other words, an increase in employee satisfaction does not necessarily lead to an increase in company revenue or individual productivity. Rather, measures to increase employee satisfaction lead to increased costs, which puts pressure on business performance.

[0007] Furthermore, motivation is an individual's motivation, and even if people are highly motivated, if they are disorganized and unable to work together, it does not necessarily mean that the company's productivity will increase. In other words, even if motivation can encourage individuals to take the initiative, it does not necessarily mean that it will lead to organizational results.

[0008] The present invention is based on such a social and technological background, and achieves the following objectives: The present invention aims to notify a respondent of his / her willingness to contribute to an organization. [Means for solving the problem]

[0009] In order to achieve the above object, the present invention takes the following measures. (1) A storage means for storing respondent information for identifying a respondent and question information for asking the respondent questions about duties, self-growth, health, human relations, support, recognition, philosophy strategy, organizational culture, and environment as factors that affect the willingness to contribute to the organization; an answer information receiving means for receiving, from a respondent terminal, the value of each factor that affects the willingness to contribute in response to the questions as answer information; andevery Based on the response information, an analysis is conducted according to the time series of the values ​​of each element in the past, every an analysis means for generating an analysis result of the answer, every a contribution willingness estimation means for estimating the contribution willingness of the respondent to the organization for each element; Every All respondents are asked to decide which factors are important and to draw causal relationships from the responses. every a factor estimation means for estimating the importance of each of the respondents; a notification means for notifying the willingness to contribute and the importance estimated by the willingness to contribute estimation means and the factor estimation means; history An importance model generating means for generating an estimation model by machine learning, which uses the answer information as training data and estimates parameters expressing the relationship between the answer information of all respondents and the importance of all respondents using factor analysis or Bayesian statistics. and and an importance output means for outputting the importance of one respondent. The analysis means, the contribution willingness estimation means, and the element estimation means use the estimation model to analyze the response information of one respondent and estimate the importance of the one respondent. 1. An information analysis device comprising: (2) An information analysis device according to claim 1, comprising: a residual information amount model generation means for generating an estimation model by machine learning, which uses answer information of all respondents as teacher data, and determines how long ago a question was selected from the answer information of all respondents and the answer information of each respondent, and estimates parameters expressing the relationship between the amount of remaining information, which is information for determining whether a question to be selected for a respondent is new or old, using factor analysis, Bayesian statistics, or clustering; and a remaining information amount output means for outputting the amount of remaining information of one respondent by analyzing the answer information of one respondent using the estimation model generated by the remaining information amount model generation means. (3) An information analysis device according to claim 2, comprising: a selective question model generation means for generating an estimation model by machine learning, using the importance of all respondents and / or the amount of remaining information of all respondents as teacher data, to estimate parameters expressing a relationship between the importance of all respondents and / or the amount of remaining information of all respondents and selective question information of all respondents; and a selective question output means for outputting selective question information to one respondent by analyzing the importance of one respondent and / or the amount of remaining information of one respondent using the estimation model generated by the selective question model generation means. (4) An information analysis device according to any one of claims 1 to 3, wherein the notification means notifies the importance of one respondent, all respondents, for each question, for each organization, for each department of an organization, for each group, or for each attribute of a respondent, or a combination of these. (5) An information analysis device according to claim 4, wherein the element estimation means estimates values, i.e., what is important to a respondent or to all respondents, from among vision orientation, growth orientation, expectation orientation, self-control orientation, and team orientation, and the notification means notifies the respondents of the values ​​estimated by the element estimation means. (6) An information analysis device according to claim 5, wherein the element estimation means estimates the values ​​from the respondent's attachment to the organization and the respondent's vitality in the organization, and the notification means notifies the values ​​estimated by the element estimation means separately into the attachment and the vitality. (7) The information analyzing device according to claim 5 or 6, wherein the notification means notifies the importance in the form of a graph. (8) a storage step for storing respondent information for identifying a respondent and question information for asking the respondent questions about duties, self-growth, health, human relations, support, recognition, philosophy strategy, organizational culture, and environment as factors that affect the willingness to contribute to the organization; an answer information receiving step for receiving, from the respondent terminal, the value of each factor that affects the willingness to contribute in response to the questions as answer information; and everyBased on the response information, an analysis is conducted according to the time series of the values ​​of each element in the past, every An analysis step of generating an analysis result of the answer, every a contribution willingness estimation step of estimating the contribution willingness of the respondent to the organization for each element; Every All respondents are asked to decide which factors are important and to draw causal relationships from the responses. every a notification step of notifying the willingness to contribute and the importance estimated by the willingness to contribute estimation step and the element estimation step; history An importance model generation process for generating an estimation model by machine learning, using the answer information as training data and estimating parameters expressing the relationship between the answer information of all respondents and the importance of all respondents using factor analysis or Bayesian statistics. and an importance output step of outputting the importance of one respondent; The analysis step, the contribution willingness estimation step, and the element estimation step use the estimation model to analyze the response information of one respondent and estimate the importance of the one respondent. 2. An information analysis method comprising: (9) A storage function for storing respondent information identifying a respondent and question information for asking the respondent questions about duties, self-growth, health, human relations, support, recognition, philosophy strategy, organizational culture, and environment as factors that affect the willingness to contribute to the organization; an answer information receiving function for receiving, from the respondent terminal, the value of each factor that affects the willingness to contribute in response to the questions as answer information; and every Based on the response information, an analysis is conducted according to the time series of the values ​​of each element in the past, every An analysis function that generates an analysis result of the answer, and every A contribution motivation estimation function estimates the contribution motivation of the respondent for each element of the organization, and from the contribution motivation estimated by the contribution motivation estimation function, Every All respondents are asked to decide which factors are important and to draw causal relationships from the responses. every A function for notifying the willingness to contribute and the importance estimated by the element estimation function and the element estimation function, and a function for notifying the willingness to contribute and the importance estimated by the element estimation function and the element estimation function, historyAn importance model generation function that uses answer information as training data and generates an estimation model by machine learning that estimates parameters expressing the relationship between the answer information of all respondents and the importance of all respondents using factor analysis or Bayesian statistics. and An importance output function that outputs the importance of one respondent; The analysis function, the contribution motivation estimation function, and the element estimation function use the estimation model to analyze the response information of one respondent and estimate the importance of the one respondent. An information analysis program that enables a computer to achieve this. Effect of the Invention

[0010] As described above, the present invention can notify respondents of their willingness to contribute to an organization based on the analysis results of responses collected from the respondents. [Brief description of the drawings]

[0011] [Figure 1] FIG. 2 is a diagram illustrating an overview of services provided by the information analysis device 1. [Diagram 2] 1 is a block diagram showing a configuration of an information analysis device 1. FIG. [Diagram 3] FIG. 2 is a block diagram showing functions of the information analysis device 1. [Figure 4] FIG. 2 is a diagram showing an example of a logical configuration of an employee information database 108. [Diagram 5] FIG. 2 shows an example of a logical configuration of a distribution information database 109.

[0012] [Figure 6] FIG. 2 is a diagram showing an example of a logical configuration of a question information database 110. [Figure 7] FIG. 2 is a diagram showing an example of a logical configuration of a selected question information database 111. [Figure 8] FIG. 2 is a diagram showing an example of a logical configuration of a response information database 112. [Figure 9] FIG. 2 is a diagram showing an example of a logical configuration of an activity plan information database 113. [Figure 10] 2 is a diagram showing an example of a logical configuration of an activity evaluation information database 114. FIG.

[0013] [Figure 11] 11 is a sequence chart for explaining a procedure for a human resources officer H to perform an initial setting process using an information analysis system W. [Figure 12] 10 is a flowchart for explaining a procedure for a model generation process performed by the main system 100. [Figure 13] FIG. 2 is a block diagram showing a learning device that executes a model generation process. [Figure 14] 10 is a flowchart for explaining a procedure for the main system 100 to perform a question selection process. [Figure 15] FIG. 2 is a block diagram showing a learner that performs multiple choice question machine learning processing.

[0014] [Figure 16] 13 is a flowchart for explaining a procedure for main system 100 to process a questionnaire. [Figure 17] FIG. 13 is a diagram showing an example of a question input screen. [Figure 18] 11 is a flowchart for explaining a procedure for main system 100 to perform analysis result output processing. [Figure 19] FIG. 2 is a block diagram showing a learning device that executes feature output processing. [Figure 20] 13 is a sequence chart for explaining a procedure in which information analysis system W transmits analysis results to respondents A, B, and C.

[0015] [Figure 21] FIG. 13 is a diagram showing an analysis result output screen for one respondent. [Figure 22] FIG. 13 is a diagram showing a group analysis result output screen. [Figure 23] 11 is a flowchart for explaining a procedure for main system 100 to perform analysis result output processing. [Figure 24] FIG. 13 is a diagram showing an activity plan information input screen. [Diagram 25] FIG. 13 is a diagram showing an analysis result output screen.

[0016] [Figure 26] FIG. 13 is a diagram showing an activity evaluation information output screen. [Figure 27] 13 is a flowchart for explaining a procedure for the main system 100 to perform a second question selection process. [Figure 28] 13 is a flowchart for explaining a procedure for main system 100 to carry out a second questionnaire process. [Figure 29] FIG. 13 is a diagram showing an activity evaluation information input screen. [Diagram 30] 10 is a flowchart for explaining a procedure for the main system 100 to perform a tallying process. [Diagram 31] FIG. 13 is a diagram showing an output screen of analysis results of groups and individuals. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Hereinafter, an information analysis device of the present invention will be described in detail with reference to FIGS. 1 to 30, taking as an example an embodiment in which the information analysis device is applied to a company that conducts questionnaires with employees as respondents. [Overview of the embodiment] The information analysis device 1 of this embodiment accepts and analyzes response information, which is responses to a questionnaire, from respondents, and generates an analysis result for each respondent. The information analysis device 1 displays the analysis results on the respondent terminal of the human resources person in charge, the respondent terminal of the manager, and the respondent terminal of the respondent. The information analysis device 1 displays, as the analysis result of the questionnaire, the level of willingness to contribute to the company for each respondent. According to this embodiment, from the analysis results obtained by the information analysis device 1, a human resources person, a manager, or a respondent can understand what the respondent is motivated to contribute to the company.

[0018] [Details of the embodiment] First Embodiment A first embodiment of the present invention will be described in detail with reference to the drawings. FIG. 1 is a diagram showing an outline of a service provided by an information analysis device 1. An information analysis system W according to the first embodiment of the present invention is a system in which an information analysis device 1 analyzes responses to a questionnaire collected from respondents A, B, and C, and changes questions to be sent to respondents A, B, and C next based on the analysis results. The information analysis system W is configured, for example, by connecting an information analysis device 1, a human resources manager terminal 2, and respondent terminals 3a, 3b, and 3c via a communication network such as the Internet. The communication network is configured, for example, using the Internet, and can perform communication that is concealed from the outside using an appropriate encryption protocol such as SSL (Secure Sockets Layer). In addition, since these devices and terminals are connected via the Internet, they can be used by people and corporations all over the world across borders without being restricted by location.

[0019] [Information analysis device 1] FIG. 2 is a block diagram showing the configuration of the information analysis device 1. The information analysis device 1 is a device such as a server that presents questionnaire questions, accepts questionnaire responses, tallys the responses, and analyzes the responses. The information analysis device 1 is configured with three systems, a main system 100, a tallying system 200, and an analysis system 300, each of which is configured with a separate server. Note that the information analysis device 1 is configured with three systems each of which is configured with an independent separate server, but it can also be realized with a single device configuration, or with a system configuration in which the systems are distributed across multiple devices. Also, the information analysis device 1 assigns certain functions (processing) to certain devices, but it is possible to freely change which functions (processing) of the three systems are assigned to which devices.

[0020] [Main System 100] The main system 100 is a server device that functions as a questionnaire support server, and provides a function of periodically conducting questionnaires for respondents A, B, and C who are employees of a company. The main system 100 provides a function of having the tallying system 200 tally the responses and the analysis system 300 analyze the responses in response to a request from a person having a certain authority in the company. The main system 100 also provides a function of presenting different questions to each of the respondents A, B, and C in response to the tallying results of the tallying system 200 and the analysis results of the analysis system 300. Furthermore, the main system 100 provides a function of setting an action plan and managing the progress of the action plan in response to a request from a respondent who has confirmed the tallying results and the analysis results. The main system 100 may be installed in an organization that conducts questionnaires, or may be installed in an ASP (Application Service Provider) or cloud managed by a service company that provides the information analysis system W.

[0021] Main system 100 includes an employee information database 108, a distribution information database 109, a question information database 110, a selected question information database 111, an answer information database 112, an activity plan information database 113, and an activity evaluation information database 114. Main system 100 uses employee information database 108, distribution information database 109, question information database 110, etc. to distribute questions to respondents who are company employees at appropriate times.

[0022] The main system 100 configured in this manner performs the following processes. (1) Employee information, which is information about company employees, is received in the employee information database 108 from the personnel manager terminal 2. (2) The distribution information database 109 receives distribution information such as the timing of distribution of questions from the personnel officer terminal 2. (3) A question selected from the question information database 110 for each of the respondents A, B, and C is stored in the selected question information database 111. (4) Questions are presented to the respondent terminals 3a, 3b, and 3c for each of the respondents A, B, and C, and responses are requested. (5) The answers received from the answerer terminals 3a, 3b, and 3c are accepted in the answer information database 112. (6) The analysis system 300 analyzes the responses to analyze the importance and amount of remaining information. (7) The importance analyzed by analysis system 300 is stored in importance database 310, and the analyzed remaining information amount is stored in a remaining information amount database. (8) In response to a request from the human resources person terminal 2 or the respondent terminal 3a having the specified authority, the tabulation system 200 tabulates the responses and transmits the tabulated results to the human resources person terminal 2 or the respondent terminal 3a.

[0023] 2(a) is a block diagram for explaining the hardware configuration of the main system 100. The main system 100 is configured by connecting a CPU (Central Processing Unit) 101, a RAM (Random Access Memory) 102, a ROM (Read Only Memory) 103, a communication control unit 104, an input unit 105, an output unit 106, a storage unit 107, and the like via a bus line. The CPU 101 is a central processing unit that operates according to a survey support program (not shown) stored in the storage unit 107, and performs information processing such as registering respondents, providing questions to respondents, collecting responses, making a tabulation request to the tabulation system 200, making an analysis request to the analysis system 300, setting an action program, and managing the progress of an action plan.

[0024] The RAM 102 is a readable and writable memory, and provides a working memory when the CPU 101 performs each process. The ROM 103 is a read-only memory, and stores basic programs and parameters for operating the CPU 101. The communication control unit 104 connects the main system 100 to the Internet. The main system 100 can communicate with the human resources staff terminal 2, the respondent terminals 3a, 3b, and 3c, the tabulation system 200, and the analysis system 300 via the communication control unit 104.

[0025] The input unit 105 is a device for inputting data from the outside, and includes hard keys for receiving input from a keyboard, an external camera for reading a predetermined image, and a touch panel for receiving data input from a soft key. The output unit 106 includes a liquid crystal display for displaying a menu screen, an image of a compilation result, an image of an analysis result, an image related to printing, and the like, and a speaker for issuing instructions and warning sounds. The storage unit 107 is configured using a large-capacity storage medium such as a hard disk or a solid state drive, and stores a survey support program, an employee information database 108, a distribution information database 109, a question information database 110, a selected question information database 111, a response information database 112, an activity plan information database 113, an activity evaluation information database 114, and the like.

[0026] [Counting system 200] The tallying system 200 is a server device that functions as a questionnaire tallying server, and provides a function of tallying answers given by respondents A, B, and C. The tallying system 200 provides a function of tallying answers by respondent, by question, by predetermined period, by department within the company, and a combination of these, in response to a request from a person with a certain authority in the company (e.g., the human resources person terminal 2, respondent A). The tallying system 200 transmits the tallying results to the main system 100 or the analysis system 300 in response to a request from the main system 100. The tallying system 200 may be installed in the organization that conducts the questionnaire, or in an ASP or cloud managed by a service company that provides the information analysis system W.

[0027] The tallying system 200 includes a tallying information database 209 for storing tallying results, etc. The tallying system 200 tallys the answer information using the answer information stored in the answer information database 112 stored in the main system 100, and stores the answer information in the tallying information database 209.

[0028] 2(b) is a block diagram for explaining the hardware configuration of the tallying system 200. The tallying system 200 is configured by connecting a CPU 201, a RAM 202, a ROM 203, a communication control unit 204, a storage unit 207, etc. via a bus line. The CPU 201 is a central processing unit, and operates according to a tallying program 208 stored in the storage unit 207, and performs information processing such as tallying up answer information.

[0029] The RAM 202 is a readable and writable memory, and provides a working memory when the CPU 201 performs each process. The ROM 203 is a read-only memory, and stores basic programs and parameters for operating the CPU 201. The communication control unit 204 connects the tallying system 200 to the Internet. The tallying system 200 can communicate with the human resources staff terminal 2, the respondent terminals 3a, 3b, and 3c, the main system 100, and the analysis system 300 via the communication control unit 204.

[0030] The storage unit 207 is configured using a large-capacity storage medium such as a hard disk or a solid state drive, and stores a tallying program 208, a tallying information database 209, and the like.

[0031] [Analysis System 300] The analysis system 300 is a server device that functions as a questionnaire analysis server, and provides a function for analyzing answers given by respondents A, B, and C. The analysis system 300 provides a function for performing analysis by respondent, by question, by department within a company, and a combination of these, according to a preset analysis time. The analysis system 300 transmits the results of the analysis to the main system 100 in response to a request from the main system 100. The analysis system 300 may be installed within the organization that conducts the questionnaire, or may be installed in an ASP (Application Service Provider) or cloud managed by a service company that provides the information analysis system W.

[0032] Analysis system 300 includes importance database 310 that stores importance analyzed from the answers of respondents, and remaining information amount database 311. Analysis system 300 uses answer information stored in answer information database 112 to analyze the answer information.

[0033] 2(c) is a block diagram for explaining the hardware configuration of analysis system 300. Analysis system 300 is configured by connecting CPU 301, RAM 302, ROM 303, communication control unit 304, importance analysis means 305, remaining information amount analysis means 306, storage unit 307, etc. via a bus line. CPU 301 is a central processing unit, and operates according to importance analysis program 308 and remaining information amount analysis program 309 stored in storage unit 307, and performs information processing such as analysis of answer information.

[0034] The RAM 302 is a readable and writable memory, and provides a working memory when the CPU 301 performs each process. The ROM 303 is a read-only memory, and stores basic programs and parameters for operating the CPU 301. The communication control unit 304 connects the analysis system 300 to the Internet. The analysis system 300 can communicate with the human resources staff terminal 2, the respondent terminals 3a, 3b, and 3c, the main system 100, and the tabulation system 200 via the communication control unit 304.

[0035] The importance analysis program 308 is a learning device for generating an estimated model, acquiring features, and outputting importance. In this embodiment, the importance analysis program 308 performs machine learning according to a learning model constructed by, for example, factor analysis, Bayesian statistics (Bayesian network and information theory), clustering, etc. The importance is information for determining the time series transition of past answers from the answer information of each answerer, and deriving a causal relationship from the answer information as to which item the answerer considers important. For example, by acquiring the importance of answerer A, it is possible to determine the time series transition of past answers (what value was entered into which item) from the answer information of answerer A, and derive which item of the nine key drivers answerer A considers important for the company and / or organization.

[0036] The remaining information amount analysis program 309 is a learning device for generating an estimation model, acquiring features, and outputting the remaining information amount. In this embodiment, the remaining information amount analysis program 309 performs machine learning according to a learning model constructed by, for example, factor analysis, Bayesian statistics (Bayesian network and information amount theory), clustering, etc. The remaining information amount is information for determining how long ago a question was selected from the answer information of each respondent and determining whether a question to be selected for the respondent is new or old. For example, by acquiring the remaining information amount of respondent A, it is possible to determine from the answer information of respondent A whether the question was just asked in the previous questionnaire or whether it was asked a long time ago and should be asked again, and to derive whether the question should be selected for respondent A or not.

[0037] The storage unit 307 is configured using a large-capacity storage medium such as a hard disk or a solid state drive, and stores an importance analysis program 308, a remaining information amount analysis program 309, an importance database 310, a remaining information amount database 311, and the like.

[0038] FIG. 3 is a block diagram showing the functions of the information analysis device. FIG. 3(a) is a block diagram for explaining the functional configuration of main system 100. Main system 100 includes, for example, a question presenting means, a question selecting means, and an answer information receiving means. The question presenting means is a means for presenting a question to an answerer terminal. The question selecting means is a means for selecting a question according to the importance. The answer information receiving means is a means for receiving answer information from an answerer terminal. FIG. 3(b) is a block diagram for explaining the functional configuration of counting system 200. Counting system 200 includes, for example, counting means. The counting means is a means for performing counting using answer information.

[0039] FIG. 3(c) is a block diagram for explaining the functional configuration of the analysis system 300. The analysis system 300 includes, for example, an importance model generating means, an importance output means, a remaining information amount model generating means, and a remaining information amount output means. The importance model generating means is a means for generating an importance model using the answer information as teacher data. The importance output means is a means for outputting importance using the importance model and the importance feature amount. The remaining information amount model generating means is a means for generating a remaining information amount model using the answer information as teacher data. The remaining information amount output means is a means for outputting the remaining information amount using the remaining information amount model and the remaining information amount feature amount.

[0040] [Human Resources Officer Terminal 2] The human resources officer terminal 2 is a terminal used by the human resources officer H to register the implementation of a survey, determine the subjects to which the survey will be conducted, and set the distribution date and time of the survey. The human resources officer terminal 2 is composed of a personal computer or the like equipped with a browser and application software for managing surveys. The human resources officer H is an employee of the company that is the subject of analysis by the information analysis system W, and is a person who is authorized to manage the information analysis system W in the department in charge of human resources. The human resources officer terminal 2 is granted a certain authority and can access the information analysis device 1 and view the tabulation results and analysis results of the survey. In addition, the human resources officer terminal 2 can compare and view the tabulation results and analysis results of the survey conducted in the past and the tabulation results and analysis results of the survey conducted this time. In addition, the human resources officer terminal 2 can compare and view the tabulation results and analysis results of the survey conducted in one department and the tabulation results and analysis results of the survey conducted in another department.

[0041] [Respondent terminal 3] The respondent terminal 3 is a terminal used for inputting responses to the questionnaire, and respondent terminals 3a, 3b, and 3c are provided for respondents A, B, and C, respectively. The respondent terminal 3 is composed of a personal computer equipped with a browser and application software for responding to questionnaires. Respondents A, B, and C are employees of the company that is the subject of analysis by the information analysis system W. The respondent terminals 3a, 3b, and 3c are also used for viewing, setting, evaluating, and the like of action plans.

[0042] The respondent terminal 3a is also a terminal used by respondent A who has certain authority as a company administrator (e.g., manager, etc.). The respondent terminal 3a is granted certain authority and can access the information analysis device 1 to analyze survey results and view the analysis results. The respondent terminal 3a can also compare and view the analysis results of surveys conducted in the past and the analysis results of the current survey. Furthermore, the respondent terminal 3a can compare and view the analysis results of surveys conducted in the department to which the respondent belongs and the analysis results of surveys conducted in other departments.

[0043] [Employee Information Database 108] Fig. 4 is a diagram showing an example of the logical configuration of employee information database 108. Note that this diagram is an example for the purpose of explanation, and the actual employee information database 108 is configured as a relational database that is logically related to other databases, etc.

[0044] The employee information database 108 is composed of items such as "no", "ID", "name", "email address", "gender", "date of birth", "job type", "job title", "employment category", "new graduate / mid-career", "date of joining the company", "group", "department", "management authority", "group authority", etc. "no" is an item for registering the order in which employees were registered in the information analysis device 1, and each item is registered with a number in the order in which they were registered. "ID" is an item for uniquely identifying employees of the company registered in the information analysis device 1, and unique ID information given to each input name is registered. "Name" is an item for registering the names of employees of the company in the information analysis device 1, and the input name is registered. As for names, both Japanese for domestic use and foreign language for overseas use can be registered.

[0045] "Email address", "Gender", "Date of birth", "Job type", "Title", "Employment category", "New graduate / mid-career", "Date of joining", "Affiliated group", and "Department" are items for registering personal information of employees of the company registered in the information analysis device 1, and in this example, the personal information of each employee entered by human resources officer H in a specified format is registered. "Administrative authority" and "Group authority" are items that indicate whether or not employees of the company registered in the information analysis device 1 have the authority to use specified functions of the information analysis device 1, and it is registered that only specified employees have been given the authority.

[0046] Other employee information registered in the employee information database 108 includes employee photographs and self-introductions (profiles), credit card information for paying system usage fees and the like to the operator of the information analysis device 1, uniform resource locators (URLs) for personal websites (homepages), social networking service (SNS) addresses, etc.

[0047] [Distribution Information Database 109] Fig. 5 is a diagram showing an example of the logical configuration of distribution information database 109. Note that this diagram is an example for the purpose of explanation, and the actual distribution information database 109 is configured as a relational database that is logically related to other databases, etc.

[0048] The distribution information database 109 is composed of items such as "distribution ID," "registration date and time," "distribution frequency," "distribution timing," "answer period," "automatic reminder," and "initial distribution of all items." "Distribution ID" is an item of distribution information uploaded and registered by the human resources person H in the information analysis device 1, and ID information assigned to the distribution information in the information analysis device 1 is registered. "Registration date and time" is an item of registration date and time when the human resources person H uploaded and registered the distribution information in the information analysis device 1, and information on the time measured by a clock installed in the information analysis device 1 is registered. "Distribution frequency" is an item indicating the distribution frequency of questions determined by the human resources person H, and information on the distribution frequency entered from the human resources person terminal 2 is registered.

[0049] "Delivery timing" is an item indicating the date and time for delivering the question determined by the human resources person H, and information on the delivery timing entered from the human resources person terminal 2 is registered. "Answer period" is an item indicating the answer period for the questionnaire determined by the human resources person H, and information on the answer period entered from the human resources person terminal 2 is registered. "Automatic reminder" is an item indicating the timing for sending a reminder to the respondent terminals 3a, 3b, 3c determined by the human resources person H, and information on the automatic reminder entered from the human resources person terminal 2 is registered. "Initial delivery of all items" is an item for allowing the human resources person H to select the question to be sent initially to the respondent terminals 3a, 3b, 3c determined by the human resources person H, and information to the effect that all items will not be delivered initially entered from the human resources person terminal 2 is registered.

[0050] [Question Information Database 110] Fig. 6 is a diagram showing an example of the logical configuration of the question information database 110. Note that this diagram is an example for the purpose of explanation, and the actual question information database 110 is configured as a relational database that is logically related to other databases, etc.

[0051] The question information database 110 is composed of items such as "no", "frequency", "question", "item", and "answer selection item (weight)". "No" is an item for registering the order in which questions were registered in the information analysis device 1, and each item is registered with a number indicating the order in which they were registered. "Frequency" is a value indicating the frequency at which questions are sent to employees who will answer questions, and a predetermined value is registered according to the content and nature of the "question" or "item". "Question" is an item of a question sent to employees who will answer questions, and registered are questions to confirm the employee's psychological state regarding work and / or the organization, questions to confirm the employee's willingness to contribute to work and / or the organization, and questions to confirm the elements that the employee considers important in the willingness to contribute.

[0052] "Items" are items that indicate nine elements (key drivers) that classify questions according to predetermined criteria, and are registered as "duties," "self-growth," "health," "human relationships," "support," "recognition," "philosophy and strategy," "organizational culture," and "environment." "Items" are elements that influence motivation to contribute to work and / or the organization, and they measure what the respondent considers important from these nine key drivers, and the measurement result is calculated as a score. "Answer selection items (weights)" are items that the respondent selects based on their answer to the question, and numbers from 1 to 7 are registered.

[0053] The "questions" registered in the question information database 110 in this example present a plurality of questions to the respondents to measure the psychological state of each respondent regarding their work and / or the organization, the willingness of each respondent to contribute to their work and / or the organization, the factors that each respondent considers important, etc., and accept answers to the questions. The questions presented to the respondents are intended to measure the importance of what causes the respondent to demonstrate their willingness to contribute to an organization such as a company through factors such as "duty," "self-growth," "health," "human relationships," "support," "recognition," "philosophy and strategy," "organizational culture," and "environment."

[0054] [Selected Question Information Database 111] 7(a) and (b) are diagrams showing an example of the logical configuration of the selected-question information database 111. Note that this diagram is an example for the purpose of explanation, and the actual selected-question information database 111 is configured as a relational database that is logically related to other databases, etc.

[0055] The selected question information database 111 is composed of items such as "Question date and time," "Answer date and time," "ID," "Question 1" to "Question 16," etc. The items "Question 1" to "Question 16" are further divided into items "Question" and "Answer." The "Question date and time" is an item indicating the date and time when the information analysis device 1 distributes a question to a respondent, and each of the items is registered with a date and time linked to the "distribution timing" registered in the distribution information database 109. The "Answer date and time" is an item indicating the date and time when the information analysis device 1 receives an answer from a respondent, and each of the items is registered with information of the time measured by a clock provided in the information analysis device 1. The "ID" is an item indicating an employee who will be the respondent to whom the information analysis device 1 distributes a question, and each of the items is registered with the ID of an employee linked to the "ID" registered in the employee information database 108.

[0056] "Question" from "Question 1" to "Question 16" is an item indicating the question that the information analysis device 1 will deliver to the respondent, and each of these is registered with a question linked to a "no" selected from the question information database 110. "Answer" from "Question 1" to "Question 16" is an item indicating the value of the answer to the question delivered to the respondent, and each is registered with a value (1 to 7) entered from the respondent terminals 3a, 3b, 3c.

[0057] As shown in Figures 7(a) and (b), "Questions" from "Question 1" to "Question 16" are selected from question information database 110 according to predetermined conditions, and the questions to be registered are different. For example, when a company is using information analysis system W for the first time and wants to ask predetermined certain questions to employees (respondents), questions are selected from "no" questions in question information database 110 so that the same questions are delivered, as shown in Figure 7(a).

[0058] On the other hand, if the company is already using the information analysis system W and has a certain amount of data for each employee, the question selection means selects from the "no" questions selected from the question information database 110 so that different questions are delivered to each employee, as shown in Fig. 7(b). Also, even if the company is using the information analysis system W for the first time, if the company does not want to ask predetermined questions to employees, the questions are selected from the "no" questions in the question information database 110 so that random questions are delivered according to the "frequency" of the question information database 110.

[0059] [Response Information Database 112] 8(a) and (b) are diagrams showing an example of the logical configuration of the response information database 112. Note that this diagram is an example for the purpose of explanation, and the actual response information database 112 is configured as a relational database that is logically related to other databases, etc.

[0060] The answer information database 112 is composed of items such as "question date and time," "answer date and time," "ID," "Question 1" to "Question 16," etc. The items "Question 1" to "Question 16" are further divided into items "Question" and "Answer." The "Question date and time" is an item indicating the date and time when the information analysis device 1 distributes a question to the answerer, and each of the dates and times linked to the "distribution timing" registered in the distribution information database 109 is registered. The "Answer date and time" is an item indicating the date and time when the information analysis device 1 receives an answer from the answerer, and each of the dates and times registered is information on the time measured by a clock provided in the information analysis device 1. The "ID" is an item indicating the employee who will be the answerer to whom the information analysis device 1 distributes a question, and each of the IDs of the employees linked to the "ID" registered in the employee information database 108 is registered.

[0061] The "Question" from "Question 1" to "Question 16" is an item indicating the question that the information analysis device 1 will deliver to the respondent, and each of these is registered with a question linked to a "no" selected from the question information database 110. The "Answer" from "Question 1" to "Question 16" is an item indicating the value of the answer to the question delivered to the respondent, and each is registered with a value (1 to 7) input from the respondent terminals 3a, 3b, and 3c.

[0062] In this example, for convenience of explanation, the selected question information database 111 and the answer information database 112 are described as separate databases. This is to clearly explain the situation in which a question was selected for each answerer and the situation in which the answerer answered the question. However, it goes without saying that the selected question information database 111 and the answer information database 112 may be the same database to collect information.

[0063] [Activity Plan Information Database 113] Fig. 9 is a diagram showing an example of the logical configuration of the activity plan information database 113. Note that this diagram is an example for the purpose of explanation, and the actual activity plan information database 113 is configured as a relational database that is logically related to other databases, etc.

[0064] The activity plan information database 113 is composed of items such as "activity ID", "activity plan", "creator ID", "belonging group", "rating (1)" to "rating (3)". The items "rating (1)" to "rating (3)" are further divided into items of "high rating", "low rating" and "improvement plan". The "activity ID" is an item that uniquely identifies the activity plan registered in the information analysis device 1, and ID information given to each inputted activity plan is registered. The "activity plan" is an item for registering an activity plan in the information analysis device 1, and activity plans inputted from the respondent terminals 3a, 3b, and 3c are registered. The "creator ID" is an item indicating an employee who inputted activity information to the information analysis device 1, and the ID of the employee linked to the "ID" registered in the employee information database 108 is registered. The "belonging group" is an item indicating a group to which the employee who inputted the activity information belongs, and information linked to the "belonging group" registered in the employee information database 108 is registered.

[0065] The "high ratings" of "Rating (1)" to "Rating (3)" are items that indicate the rating of the answer to the activity information distributed to the respondent, and ratings entered from the respondent terminals 3a, 3b, and 3c are registered, respectively. The "low ratings" of "Rating (1)" to "Rating (3)" are items that indicate the rating of the answer to the question distributed to the respondent, and ratings entered from the respondent terminals 3a, 3b, and 3c are registered, respectively. The "improvement suggestions" of "Rating (1)" to "Rating (3)" are items that indicate the rating of the answer to the question distributed to the respondent, and ratings entered from the respondent terminals 3a, 3b, and 3c are registered, respectively.

[0066] In this example, for convenience of explanation, the activity plan information database 113 is exemplified by one in which different items are registered, as shown in Figures 9(a) and (b), in order to explain a situation in which no activity plan information has been input and a situation in which activity plan information has already been input.

[0067] [Activity Evaluation Information Database 114] Fig. 10 is a diagram showing an example of the logical configuration of the activity evaluation information database 114. Note that this diagram is an example for the purpose of explanation, and the actual activity evaluation information database 114 is configured as a relational database that is logically related to other databases, etc.

[0068] The activity evaluation information database 114 is composed of items such as "Activity ID," "Activity Plan," "Creator ID," "Rating (1)" to "Rating (3)," etc. The items "Rating (1)" to "Rating (3)" are further divided into items such as "High Rating," "Low Rating," and "Improvement Suggestion." "Activity ID" is an item that uniquely identifies an activity plan registered in the information analysis device 1, and ID information assigned to each input activity plan is registered. "Activity Plan" is an item that registers an activity plan in the information analysis device 1, and activity plans input from respondent terminals 3a, 3b, 3c are registered.

[0069] "Creator ID" is an item that indicates the employee who inputted activity information to the information analysis device 1, and each registered ID of the employee is linked to the "ID" registered in the employee information database 108. "Group" is an item that indicates the group to which the employee who inputted the activity information belongs, and each registered information is linked to the "Group" registered in the employee information database 108.

[0070] The "high ratings" of "rating (1)" to "rating (3)" are items that indicate the rating of the answer to the activity information distributed to the respondent, and the ratings entered from the respondent terminals 3a, 3b, and 3c are registered, respectively. The "low ratings" of "rating (1)" to "rating (3)" are items that indicate the rating of the answer to the question distributed to the respondent, and the ratings entered from the respondent terminals 3a, 3b, and 3c are registered, respectively. The "improvement suggestions" of "rating (1)" to "rating (3)" are items that indicate the rating of the answer to the question distributed to the respondent, and the ratings entered from the respondent terminals 3a, 3b, and 3c are registered, respectively.

[0071] In this example, the activity plan information database 113 and the activity evaluation information database 114 are described as separate databases for the sake of convenience. This is to clearly explain the situation in which an activity plan is input for each respondent and the situation in which the respondent evaluates the input activity plan. However, it goes without saying that the activity plan information database 113 and the activity evaluation information database 114 may be the same database to collect information. Also, in this example, for the sake of convenience, the activity evaluation information database 114 is illustrated as one in which different items are registered, as shown in Figures 10(a) and (b). This is to explain the situation in which no activity evaluation information is input and the situation in which activity evaluation information has already been input.

[0072] [Aggregated Information Database 209] The aggregated information database 209 is configured by dividing it into items according to the aggregation results of the answer information in the answer information database 112 (not shown). The aggregated information database 209 stores the aggregation results obtained by aggregating values ​​obtained from answers to questions from respondents. The aggregated information database 209 may store aggregation results according to each respondent, each question, each organization, each department of an organization, each attribute of the respondent, or the like. The aggregated information database 209 may store an average value of the aggregation results of each item for each group indicating the characteristics of the diagnostic area.

[0073] [Importance Database 310] The importance database 310 is configured by dividing items according to the answer information of the answer information database 112 (not shown). The importance database 310 stores the importance of each answerer by analyzing values ​​obtained from the answer information. The importance database 310 may store the importance according to each answerer, each question, each organization, each department of an organization, each attribute of the answerer, etc.

[0074] [Remaining Information Database 311] The remaining information amount database 311 is configured by dividing it into items according to the answer information of the answer information database 112 (not shown). The remaining information amount database 311 stores the remaining information amount for each answerer by analyzing the freshness of information obtained from the answer information. The remaining information amount database 311 may store the remaining information amount according to each answerer, each question, each organization, each department of an organization, each attribute of the answerer, etc.

[0075] [Operation of information analysis system W] Next, we will explain the operation of the information analysis system W configured as above. The operations of various processes described below are realized by the CPU 101 of the main system 100, the CPU 201 of the tabulation system 200, the CPU 301 of the analysis system 300, etc., executing various programs stored in the respective memories 107, 207, 307.

[0076] [Initial setting process] 11 is a sequence chart for explaining the procedure for initial setting processing performed by the human resources officer H using the information analysis system W. The initial setting processing is processing for making initial settings for using the information analysis system W. The following processing is performed by the CPU 101 and the CPU of the human resources officer terminal 2 according to the initial setting program.

[0077] [System Registration Request SC1] First, the CPU of the personnel manager terminal 2 accesses the top screen of the portal site of the main system 100 through the browser operation of the personnel manager H. The main system 100 then accepts the access via the communication control unit 104, generates top screen data for displaying the top screen in the RAM 102, and transmits this to the personnel manager terminal 2. When the CPU of the personnel manager terminal 2 receives the top screen data, it stores it in the RAM and uses it to display the top screen on the output unit.

[0078] When the registration button on the top screen is selected by the human resources person H, the CPU of the human resources person terminal 2 transmits a request for human resources person H registration screen data to the main system 100. Furthermore, upon receiving this request, the CPU 101 generates registration screen data for human resources person H in the RAM 12 and transmits it to the human resources person terminal 2. Upon receiving the human resources person H registration screen data from the main system 100, the CPU of the human resources person terminal 2 stores it in the RAM and uses it to display the human resources person H registration screen on the output section.

[0079] When the human resources person H enters employee information into the human resources person H registration screen, the CPU of the human resources person terminal 2 stores the employee information in RAM. When the human resources person H selects, for example, a register button, the CPU of the human resources person terminal 2 transmits the employee information stored in RAM to the main system 100. When the CPU 101 receives the employee information from the human resources person terminal 2, it registers it in the employee information database 108.

[0080] [ID / PW assignment SC2] CPU 101 generates a unique ID and password PW for human resources person H and registers them in employee information database 108. CPU 101 sends the generated ID and password PW by email to human resources person terminal 2. Through the above process, human resources person H is given a unique ID and password and becomes able to log in to his / her personal page in information analysis system W.

[0081] [Receiving respondent information SC3] When the human resources person H returns to the top screen by operating the browser, enters the ID and password PW of the human resources person H in the ID and password fields and selects the login button, the CPU of the human resources person terminal 2 sends the ID and password PW of the human resources person H to the main system 100 and requests the human resources person H to log in to his / her personal page. The CPU 101 stores the ID and password of the human resources person H in the RAM 12 and performs the login process by comparing these with the ID and password PW of the human resources person H stored in the employee information database 108.

[0082] After authenticating the human resources person H, the CPU 101 generates, in the RAM 12, MyPage screen data for displaying a MyPage screen for the human resources person H from the information registered in the employee information database 108. Then, the CPU 101 transmits the generated MyPage screen data to the human resources person terminal 2.

[0083] The CPU of the human resources personnel terminal 2 receives the My Page screen data from the main system 100 and stores it in the RAM. The CPU of the human resources personnel terminal 2 then displays the My Page screen 501 on the output section using the stored My Page screen data. When the human resources personnel H selects, for example, an employee registration button on the My Page screen 501, the CPU of the human resources personnel terminal 2 transmits a request for employee registration screen data to the main system 100. Upon receiving the request, the CPU 101 generates employee registration screen data and transmits it to the human resources personnel terminal 2.

[0084] When the CPU of the personnel manager terminal 2 receives the employee registration screen data from the main system 100, it stores it in the RAM. Then, the stored employee registration screen data is used to display the employee registration screen on the output section. On the employee registration screen, items such as "Name", "Email address", "Sex", "Date of birth", "Occupation", "Title", "Employment category", "New graduate / mid-career", "Date of joining the company", "Group", "Department", "Administrative authority", and "Group authority" are displayed so that they can be entered for registration in the employee information database 108. The personnel manager H may enter these items from the input section of the personnel manager terminal 2, or may enter them by exporting data from a spreadsheet software that has been prepared in advance. When the personnel manager H enters employee information from the employee registration screen, the CPU of the personnel manager terminal 2 stores it in the RAM. Then, when the personnel manager H selects the registration button on the employee registration screen, the CPU of the personnel manager terminal 2 transmits the employee information stored in the RAM to the main system 100.

[0085] When the CPU 101 receives the employee information from the human resources officer terminal 2, it registers it in the employee information database 108 as shown in FIG. 4. The CPU 101 generates a unique ID and password PW for each employee as a respondent, and registers them in the employee information database 108. The CPU 101 sends the generated ID and password PW by email to the respondent terminals 3a, 3b, and 3c. Respondents A, B, and C, who are employees, are given unique IDs and passwords and are able to log in to their personal pages in the information analysis system W. In this way, the main system 100 is equipped with an employee information registration means for receiving and registering employee information, which is information on employees who will be respondents, from the human resources officer terminal 2.

[0086] [Distribution information reception SC4] When the human resources person H selects, for example, the distribution information registration button on the My Page screen 501, the CPU of the human resources person terminal 2 transmits a request for distribution information registration screen data to the main system 100. Upon receiving the request, the CPU 101 generates distribution information registration screen data and transmits it to the human resources person terminal 2. Upon receiving the distribution information registration screen data from the main system 100, the CPU of the human resources person terminal 2 stores it in the RAM. Then, the stored distribution information registration screen data is used to display the distribution information registration screen on the output section.

[0087] The distribution information registration screen displays items such as "distribution frequency," "distribution timing," "answer period," "automatic reminder," and "initial distribution of all items" that can be entered to register in the distribution information database 109. When the human resources person H enters the distribution information from the distribution information registration screen, the CPU of the human resources person terminal 2 stores it in RAM. Then, when the human resources person H selects the registration button on the distribution information registration screen, the CPU of the human resources person terminal 2 transmits the distribution information stored in RAM to the main system 100.

[0088] When main system 100 receives distribution information from human resources personnel terminal 2, it registers these items in distribution information database 109 as shown in Fig. 5. Also, when main system 100 receives distribution information from human resources personnel terminal 2, it generates a unique distribution ID as shown in Fig. 5 and registers it in "distribution ID" in distribution information database 109. Furthermore, when main system 100 receives distribution information from human resources personnel terminal 2, it measures the time using a built-in clock and registers it in "registration date and time" in distribution information database 109. In this way, main system 100 is provided with distribution information registration means for receiving and registering distribution information, which is information on the distribution of the questionnaire, from human resources personnel terminal 2.

[0089] [Model generation process] FIG. 12 is a flow chart for explaining the procedure of the model generation process performed by the main system 100. The model generation process is a learning phase in which the response information for each of all respondents stored in the response information database 112 is input and analyzed to obtain a relationship between the response information and a predetermined feature amount. The CPU 101 monitors whether the current time has reached the model generation time (S1). That is, the CPU 101 obtains the current time from a built-in clock, and monitors whether the current time has reached the date and time of the "timing of model generation" registered in the storage unit 107. When the current time reaches the date and time of the "timing of model generation" (S101: Y), the CPU 101 requests the analysis system 300 to execute the importance model generation process (S2) and the residual information estimator model process (S3). When the analysis system 300 finishes the model generation process, the CPU 101 again monitors whether the current time has reached the analysis time (S1).

[0090] [Importance model generation process] FIG. 13(a) is a block diagram showing a learning device that executes an importance model generation process. The importance model generation process is a learning phase that obtains a relationship between answer information, which is input data, and importance, which is output data. In response to a request from CPU 101, CPU 301 inputs the historical answer information of all respondents stored in the answer information database 112 to the learning device. The learning device obtains the relationship between the answer information and the importance by analyzing the input historical answer information of all respondents. By going through such a learning phase, the learning device obtains a model regarding the importance, that is, obtains parameters that express the relationship between the answer information of all respondents and the importance. By obtaining such a model regarding the importance (hereinafter referred to as the "importance model"), the learning device becomes able to predict the importance based on a certain standard from the answer information of one answerer. In this way, the analysis system 300 is provided with an importance model generation means that uses the answer information of all respondents as teacher data and generates an estimation model by machine learning that estimates parameters that express the relationship between the answer information of all respondents and the importance.

[0091] [Residual information amount model generation process] FIG. 13(b) is a block diagram showing a learning device that executes the remaining information amount model generation process. The remaining information amount model generation process is a learning phase that obtains the relationship between the answer information, which is input data, and the remaining information amount, which is output data. In response to a request from the CPU 101, the CPU 301 inputs the historical answer information of all respondents stored in the answer information database 112 to the learning device. The learning device obtains the relationship between the answer information and the remaining information amount by analyzing the input historical answer information of all respondents. The learning device obtains a model regarding the remaining information amount by going through such a learning phase, that is, obtains parameters that express the relationship between the answer information of all respondents and the remaining information amount. By obtaining such a model regarding the remaining information amount (hereinafter referred to as the "remaining information amount model"), the learning device becomes able to predict the remaining information amount based on a certain standard from the answer information of one answerer. In this way, the analysis system 300 is provided with a remaining information amount model generation means that uses the answer information of all respondents as teacher data and generates an estimation model by machine learning that estimates parameters that express the relationship between the answer information of all respondents and the remaining information amount.

[0092] [Question selection process] FIG. 14 is a flow chart for explaining the procedure of the question selection process performed by the main system 100. The question selection process is a process for selecting question information to be asked to each answerer. The CPU 101 monitors whether the current time has reached the distribution date and time (S101). That is, the CPU 101 obtains the current time from a built-in clock, and monitors whether the current time has reached the date and time of the "distribution timing" registered in the distribution information database 109. When the current time reaches the date and time of the "distribution timing" (S101: Y), the CPU 101 judges whether the question to be presented is the first distribution or not (S102). That is, when a company using the information analysis system W uses this system for the first time, the importance and remaining information amount for each answerer have not been learned, and therefore an appropriate question may not be selected, so it is necessary to select a question prepared in advance.

[0093] If it is not the first distribution (S102: N), the CPU 101 requests the analysis system 300 to execute a selective question machine learning process (S103). If it is the first distribution (S102: Y), the CPU 101 judges whether or not "initial all item distribution" in the distribution information database 109 is ON (S104). If "initial all item distribution" is ON (S104: Y), the CPU 101 executes a basic question output process (S105). That is, the CPU 101 reads the question information of 32 basic questions "q1" to "q32" from the items of "no" registered in the question information database 110, registers them in the selective question information for each answerer (for example, see FIG. 7(a)), and transmits them for each answerer. In this way, by setting the number of initial questions to 32, which is a relatively large number, the importance and the amount of remaining information can be obtained reliably as basic data for each answerer.

[0094] When "initial all item delivery" is OFF (S104:N), the CPU 101 executes the frequency question output process (S106). That is, the CPU 101 registers 16 question information randomly selected from the question information database 110 in the selected question information for each respondent according to the value of "frequency" registered in the question information database 110, and transmits it for each respondent. In some companies, if there are 32 initial questions, employees may not respond proactively, so by reducing the number of questions to 16, the importance and remaining information amount that are basic data for each respondent can be obtained slowly and steadily. Note that when the CPU 101 executes the frequency question output process (S106), the 16 questions selected from the question information database 110 are selected randomly and evenly according to the nine key drivers. In this way, by evenly selecting questions according to the nine key drivers, the average importance and remaining information amount can be obtained.

[0095] When the CPU 101 finishes executing any one of the selective question machine learning process (S103), the basic question output process (S105), or the frequency question output process (S106), it again monitors whether the date and time of the "distribution timing" has been reached (S101).In this way, the main system 100 includes a question selection means for selecting question information to be asked to each respondent according to the analysis result obtained by the analysis means.

[0096] [Multiple Choice Question Machine Learning Processing S103] More specifically, the multiple-choice question machine learning process (S103) shown in FIG. 14 is composed of two processes: a multiple-choice question model generation process shown in FIG. 15(a) and a multiple-choice question output process shown in FIG. 15(b). FIG. 15(a) is a block diagram showing a learning device that executes the multiple-choice question model generation process. The multiple-choice question model generation process is a learning phase that obtains the relationship between the importance and remaining information amount, which are input data, and the multiple-choice question information, which is output data. In response to a request from CPU 101, CPU 301 inputs the importance of all respondents stored in the importance database 310 and the remaining information amount of all respondents stored in the remaining information amount database 311 to the learning device.

[0097] The learning device obtains the relationship between the importance and the remaining information amount and the selective question information by analyzing all the input importances and all the remaining information amounts. The learning device obtains a model for selective question information by going through such a learning phase, that is, obtains parameters expressing the relationship between the importance and the remaining information amount of all respondents and the selective question information. By obtaining such a model for selective question information (hereinafter referred to as "selected question information model"), the learning device becomes able to select selective question information based on a certain criterion from the importance and remaining information amount of each respondent. In this way, the analysis system 300 includes a selective question model generation means that uses the importance of all respondents and / or the remaining information amount of all respondents as teacher data and generates an estimation model by machine learning to estimate parameters expressing the relationship between the importance of all respondents and / or the remaining information amount of all respondents and the selective question information of all respondents.

[0098] FIG. 15(b) is a block diagram showing a learning device that executes the selective question output process. The selective question output process is a prediction phase in which selective question information for one answerer predicted through a selective question information model is obtained from the importance of one answerer and the remaining information amount of one answerer, which are input data. In response to a request from the CPU 101, the CPU 301 inputs the importance of one answerer stored in the importance database 310 and the remaining information amount of one answerer stored in the remaining information amount database 311 to the learning device. The learning device obtains selective question information for one answerer by analyzing the input importance of one answerer and the remaining information amount of one answerer. By using the selective question information model obtained in this manner in the selective question model generation process, the learning device can select selective question information based on a certain criterion from the importance and remaining information amount of each answerer.

[0099] In this way, the analysis system 300 includes a selective question output means for outputting selective question information for one respondent by analyzing the importance of one respondent and / or the amount of remaining information of one respondent using the estimation model generated by the selective question model generation means. The CPU 101 generates the selective question information of 16 questions obtained by the CPU 301 from the learning device in the RAM 102 to be transmitted to each respondent. Note that the CPU 101 may register the selective question information of the 16 questions obtained by the CPU 301 from the learning device in the selective question information database 111 (see, for example, FIG. 7(b)), and then generate the selective question information in the RAM 102 to be transmitted to each respondent.

[0100] [Survey processing] 16 is a flow chart for explaining the procedure for the main system 100 to process a survey. The survey process is a process for sending different questions to each respondent and receiving the responses from each respondent. The following process is performed by the CPU 101 and the CPU of each of the respondent terminals 3a, 3b, and 3c according to a predetermined program.

[0101] [Question information transmission SC101] First, the CPU 101 generates question input screen data for each respondent in the RAM 102 from the selected question information generated in the RAM 102 in the selected question machine learning process (S103), the basic question output process (S105), or the frequency question output process (S106) in the question selection process, and transmits a notice that there is a survey to each of the respondent terminals 3a, 3b, and 3c. In this example, the CPU 101 notifies the respondent terminals 3a, 3b, and 3c that there is a survey and includes a URL for transitioning to the question input screen of the survey in an email to notify the respondent terminals 3a, 3b, and 3c of the presence of the survey. The main system 100 may notify the respondent terminals 3a, 3b, and 3c of the URL for transitioning to the question input screen by a chatbot, SNS, or the like other than email.

[0102] When the URL written in the email is selected by the respondent terminal 3a, 3b, 3c, the CPU of the respondent terminal 3a, 3b, 3c transmits a request for question input screen data for each of the respondents A, B, C to the main system 100. Furthermore, upon receiving the request, the CPU 101 transmits the question input screen data for each of the respondents A, B, C to the respondent terminal 3a, 3b, 3c. When the CPU of the respondent terminal 3a, 3b, 3c receives the question input screen data for each of the respondents A, B, C from the main system 100, it stores it in RAM and uses it to display the question input screen for each of the respondents A, B, C on the output section (see FIG. 17). Thus, the main system 100 is provided with a question presenting means for presenting the question information for each respondent selected by the question selecting means to the respondent terminal for each respondent.

[0103] [Question input screen] FIG. 17 is a diagram showing an example of a question input screen. The question input screen is a page where the answerers A, B, and C input answers to the questions selected by the analysis system 300 and transmitted by the main system 100 to each of the answerer terminals 3a, 3b, and 3c. The answer information stored in the answer information database 112 is updated according to the content input here (see FIG. 8). The question output field 171 is a field where question information linked to the selected question information selected for each answerer is output. The answer input field 172 is a field where the answerer inputs according to the question output in the question output field 171. The answer input field is a field where data is input to determine for each answerer, such as the state of mind of each answerer toward the work and / or organization, the state of willingness to contribute to the work and / or organization of each answerer, and the factors that each answerer considers important, based on the answer to the selected question.

[0104] In this embodiment, seven ranks from "I don't think so at all" to "I think so very much" can be input in the answer input field by selecting buttons 172a to 172g, but other ranks (7 or less, 7 or more) may be set. Respondents A, B, and C select buttons 172a to 172g from the input section of the respondent terminal 3a, 3b, and 3c. When the respondent A, B, and C input an answer from the question input screen, the CPU of the respondent terminal 3a, 3b, and 3c stores it in RAM. The input answer is converted into a numerical value according to the selected button 172a to 172g, for example, according to conversion data stored in the storage device of the respondent terminal 3a, 3b, and 3c (for example, button 172a=1, button 172b=2, button 172c=3, button 172d=4, button 172e=5, button 172f=6, button 172g=7, etc.).

[0105] [Response information reception SC102] When the answerers A, B, and C answer all the questions and select a confirmation button (not shown) to check for input errors, for example, the CPUs of the answerer terminals 3a, 3b, and 3c transmit the answer information stored in the RAM to the main system 100. When the CPU 101 receives the answer information for each of the answerers A, B, and C from the answerer terminals 3a, 3b, and 3c, it registers it in the answer information database 112 as shown in Figure 8(a) or 8(b).

[0106] The CPU 101 monitors whether the current time has reached the time for a reminder (not shown). That is, the CPU 101 obtains the current time from a built-in clock, monitors whether the current time has reached the date and time of the "automatic reminder" registered in the distribution information database 109, and when the current time reaches the date and time of the "automatic reminder", the CPU 101 generates reminder screen data in the RAM 102 and transmits it to the respondent terminals 3a, 3b, and 3c. In this way, the main system 100 is provided with an answer information receiving means for receiving answer information answered by each of the respondents A, B, and C from the respondent terminals 3a, 3b, and 3c.

[0107] [Notice of end of response period SC103] When all answerers have completed their answers or the answer period has expired, the CPU 101 generates answer period end notification screen data in the RAM 102 to display an answer period end notification screen, and transmits this to a person with a certain level of authority (e.g., the human resources person terminal 2, answerer A). For example, when the CPU of the human resources person terminal 2 or the answerer terminal 3a receives the answer period end notification screen data, it stores it in the RAM and uses it to display the answer period end notification screen on the output section.

[0108] [Analysis result output processing] 18 is a flowchart for explaining the procedure for analysis result output processing performed by main system 100. The analysis result output processing is processing for outputting analysis results for each respondent in response to a request from each respondent. CPU 101 monitors whether or not there is a request for analysis from the respondent (S201). If there is a request for analysis from the respondent (S201:Y), CPU 101 requests analysis system 300 to execute feature output processing (S202). When main system 100 finishes feature output processing (S202), CPU 101 again monitors whether or not there is a request for analysis from the respondent (S201).

[0109] In this example, the analysis result output process outputs the analysis results for each respondent in response to the request of each respondent. However, if a group is created for multiple employees within an organization and it is desired to output the analysis results for that group, the analysis results for the group may be output at a predetermined timing. For example, when the survey is completed, the analysis results for each group may be output to the employees belonging to that group.

[0110] [Feature output processing] Fig. 19(a) is a block diagram showing a learning device that executes importance output processing. The importance output processing is a prediction phase that obtains the importance of one answerer predicted through an importance model from the answer information of one answerer, which is input data. In response to a request from CPU 101, CPU 301 inputs the answer information of one answerer stored in answer information database 112 to the learning device. The learning device obtains the importance of one answerer by analyzing the input answer information of one answerer. The learning device can predict the importance of each answerer by using the importance model obtained in this manner by the importance model generation processing.

[0111] In other words, by predicting the importance of a given respondent, it is possible to predict the respondent's psychological state regarding the job and / or organization, willingness to contribute, and factors that he or she considers important. In this way, the analysis system 300 includes an importance output means that outputs the importance of a given respondent by analyzing the answer information of the given respondent using the estimation model generated by the importance model generation means. The CPU 301 registers the importance obtained from the learning device in the importance database 310.

[0112] In this way, analysis system 300 includes analysis means for analyzing the answer information received by the answer information receiving means and generating analysis results for each respondent. Analysis system 300 includes psychological state estimation means for estimating the psychological state of each respondent according to the analysis results obtained by the analysis means. Analysis system 300 also includes willingness to contribute estimation means for estimating willingness to contribute to the work and / or organization of each respondent according to the psychological state estimated by the psychological state estimation means. Analysis system 300 also includes element estimation means for estimating elements that are important to each respondent according to the contribution estimated by the willingness to contribute estimation means.

[0113] FIG. 19(b) is a block diagram showing a learning device that executes the remaining information amount output process. The remaining information amount output process is a prediction phase that obtains the remaining information amount of one answerer predicted through the remaining information amount model from the answer information of one answerer, which is input data. In response to a request from CPU 101, CPU 301 inputs the answer information of one answerer stored in answer information database 112 to the learning device. The learning device obtains the remaining information amount of one answerer by analyzing the input answer information of one answerer. By using the remaining information amount model obtained in this manner by the remaining information amount model generation process, the learning device can predict the remaining information amount for each answerer.

[0114] In other words, by predicting the remaining information amount of one answerer, it is possible to predict the questions that one answerer has answered in the past and to predict the questions that one answerer should answer. In this way, analysis system 300 includes a remaining information amount output means that outputs the remaining information amount of one answerer by analyzing the answer information of one answerer using the estimation model generated by the remaining information amount model generation means. CPU 301 registers the remaining information amount obtained from the learning device in remaining information amount database 311.

[0115] [Analysis result transmission process] 20 is a sequence chart for explaining the procedure by which information analysis system W transmits analysis results to respondents A, B, and C. The analysis result transmission process is a process for transmitting the analysis results for each respondent to each respondent. The following process is performed by CPU 101 and the CPU of each of respondent terminals 3a, 3b, and 3c according to a predetermined program.

[0116] [Send analysis results SC201] When the CPU 101 analyzes the importance of one respondent in the feature output process, it generates analysis result output screen data for the one respondent in RAM 102 according to the importance of the one respondent registered in the importance database 310, and transmits it to the respondent terminal 3a of one respondent A. When the CPU of the respondent terminal 3a receives the analysis result output screen data, it stores it in RAM and uses it to display the analysis result output screen on the output section. Note that similar processing is performed for the other respondents, respondent B and respondent C (SC202, SC203).

[0117] FIG. 21 is a diagram showing an analysis result output screen for the department (sales) to which one respondent A belongs. The analysis result output screen is a screen that organizes and displays the latest questionnaire in terms of the respondent's psychological state toward their work and / or organization, their willingness to contribute to their work and / or organization, and the factors they consider important within their willingness to contribute to their work and / or organization. In this way, the main system 100 is equipped with an analysis result presentation means that outputs the analysis results corresponding to the latest questionnaire to the respondent terminal. Respondent A accesses the analysis result output screen using, for example, a browser and checks the various analysis results.

[0118] The guideline column 21a is a column for inputting the guideline of respondent A and the department (sales) to which respondent A belongs. The engagement column 21b is a column for displaying an overall score indicating the engagement of the department (sales) to which respondent A belongs, which is calculated according to the importance obtained from the analysis results of the latest questionnaire. In addition to the score indicating the engagement, the engagement column 21b outputs a benchmark score obtained as a reference point or standard point, a value indicating the deviation from the benchmark, etc.

[0119] The transition column 21c is a column showing the transition of the score indicating the engagement of the department (sales) to which respondent A belongs. In the transition column 21c, a line graph is outputted connecting points plotted according to the date of distribution to respondent A, showing the score indicating the engagement of the department (sales) to which respondent A belongs. In the transition column 21c, a bar graph showing the score distribution is outputted. In the transition column 21c, the score indicating the engagement of the department (sales) to which respondent A belongs is outputted as a line graph according to the transition of the survey responses, making it easy to understand the trends and patterns represented by the scores.

[0120] The work driver column 21d is a column that displays the score for each key driver that indicates the engagement that respondent A feels toward the work in the department (sales) to which he / she belongs, obtained from the responses to the latest questionnaire or the latest and past questionnaires. The work driver column 21d outputs scores indicating engagement related to "recognition," "human relationships," "support," "health," "self-growth," and "duties" out of the nine key drivers. In addition to the scores indicating engagement related to the six key drivers, the work driver column 21d also outputs benchmark scores obtained as reference points or standard points for these, values ​​indicating deviations from the benchmark, and the like.

[0121] The work driver change column 21e is a column for outputting key drivers that have changed among the six key drivers, divided into important increases and important decreases. The work driver evaluation column 21f is a column for outputting a written evaluation of key drivers that have changed among the six key drivers.

[0122] The organizational driver column 21g is a column that displays the score for each key driver that indicates the engagement felt by respondent A with respect to the organizational philosophy, culture, and environment of the department (sales) to which he / she belongs, obtained from the responses to the latest questionnaire or the latest and past questionnaires. The organizational driver column 21g outputs scores indicating engagement related to "organizational culture," "philosophy strategy," and "environment" out of the nine key drivers. In addition to the scores indicating engagement related to the three key drivers, the organizational driver column 21g also outputs benchmark scores obtained as reference points or standard points, values ​​indicating deviations from the benchmark, and the like.

[0123] The organizational driver change field 21h is a field for outputting key drivers that have changed among the three key drivers, divided into important increases and important decreases. The organizational driver evaluation field 21i is a field for outputting a written evaluation of key drivers that have changed among the three key drivers.

[0124] By viewing the work driver change column 21e, the work driver evaluation column 21f, the organizational driver change column 21h, and the organizational driver evaluation column 21i, respondent A can easily grasp changes obtained from the analysis results of the latest questionnaire in respondent A's psychological state regarding the work and / or organization of the department (sales) to which he / she belongs, his / her willingness to contribute to the work and / or organization, and the factors he / she considers important within his / her willingness to contribute to the work and / or organization, etc. The viewing authority column 21j is a column that outputs groups for which respondent A has viewing authority.

[0125] FIG. 22 is a diagram showing a group analysis result output screen. The score on engagement may be output by analyzing one respondent, but may be calculated and output not only for each respondent but also for each predetermined group. FIG. 22(a) is a comparative diagram showing an analysis result output screen of the department (sales) to which respondents A, B, and C belong. The first column 22a is a column for outputting the score (score) for each predetermined element indicating the engagement of respondent A, which is calculated according to the importance obtained from the analysis result of the questionnaire, for the first and second times. The second column 22b is a column for outputting the score (score) for each predetermined element indicating the engagement of respondent B, which is calculated according to the importance obtained from the analysis result of the questionnaire, for the first and second times.

[0126] The third column 22c is a column for outputting the score for each predetermined element indicating the engagement of respondent C, calculated according to the importance obtained from the analysis results of the questionnaire, for the first and second times. The importance calculation column 22d is a column for outputting the importance of each predetermined element for respondents A, B, and C. The group column 22e is a column for outputting the score for each predetermined element indicating the engagement of a predetermined group (sales), calculated according to the importance obtained from the analysis results of the questionnaire, and the importance of each predetermined element for the predetermined group (sales).

[0127] FIG. 22(b) is a scatter diagram showing an analysis result output screen for the department (sales) to which respondents A, B, and C belong. CPU 101 displays a scatter diagram in which the score of a specific group (sales) and the importance of a specific group (sales) are plotted on a two-dimensional graph with the axes being the score and the importance of the specific group (sales), respectively, according to the importance of respondents A, B, and C registered in importance database 310. By viewing such a scatter diagram, for example, it is possible to grasp the level of importance of employees in an organization and to determine whether improvement is necessary. In this way, main system 100 is provided with a notification means for notifying the respondent of his / her psychological state regarding his / her work and / or organization, his / her willingness to contribute, and the factors he / she considers important.

[0128] The score on engagement can be calculated by, for example, adding up the weighted values ​​of the importance of the respondent's organization, work, superior, and workplace, and the importance of the items related to each diagnostic area. The score on engagement may be calculated not only for each respondent, but also for each question, organization, department of the organization, group, or attribute of the respondent. The score or ranking of engagement may be displayed, for example, in comparison with other departments, groups, or attributes in the same organization, or in comparison with departments, groups, or attributes of organizations of the same regulations. By displaying in this way, when the work engagement of a department or employee of an organization is low compared to the same organization, it can be determined that, for example, some kind of activity is necessary. By displaying in this way, when the work engagement of a department or employee of an organization is high compared to the same organization, it can be determined that it is time to expand the business.

[0129] By using the information analysis system W of the first embodiment of the present invention, a small number of questionnaires (16 questions) can be administered over a short period of time (e.g., every 4 weeks) with different questions for each respondent. Such a questionnaire reduces the burden on the respondent and is likely to obtain cooperation from the respondent even if it is administered relatively frequently (every 4 weeks). Furthermore, by administering the questionnaire relatively frequently, it becomes easier to measure the psychological state of each respondent regarding their work and / or organization, the state of each respondent's willingness to contribute to their work and / or organization, and what factors each respondent considers important.

[0130] Second Embodiment In the first embodiment described above, different questions were sent to each respondent as selected question information, but in the second embodiment, in addition to the selected question information, an action plan devised by the respondent is sent to the respondent in a specified department (sales) as action plan information. In addition to the selected questions, the respondent can evaluate the action plan on the respondent terminal. In the second embodiment, it is possible to collect candid opinions from the respondent who is the person in the field who implemented the action plan, and to look back on the effectiveness of the action plan confirmed by the respondent.

[0131] The main system 100 of the second embodiment performs the following process. (1) Action plans are accepted from terminals of respondents of a predetermined group (for example, respondent terminal 3a) and stored in an activity plan information database. (2) Evaluation questions regarding the action plan are presented to the terminals of respondents in a predetermined group (for example, the respondent terminals 3a, 3b, and 3c) to request an evaluation of the action plan. (3) Evaluations of the action plan are accepted from the terminals of respondents of a predetermined group (for example, the respondent terminals 3a, 3b, 3c) and stored in an activity evaluation information database.

[0132] [Second analysis result output process] Fig. 23 is a flowchart for explaining the procedure for the main system 100 to perform the second analysis result output process. The second analysis result output process executes a process for requesting input of an activity plan and a process for outputting an evaluation of an activity plan that has already been input, in addition to a process for outputting the analysis result for each respondent in response to a request for each respondent. Note that the second analysis result output process is the above-mentioned analysis result output process (see Fig. 18) to which a certain process has been added. For this reason, only the process unique to the second analysis result output process, which is surrounded by a dashed line in Fig. 23, will be explained.

[0133] When the main system 100 finishes the feature output process (S202), the CPU 101 judges whether or not there is an activity plan (S203). That is, the CPU 101 judges whether or not there is activity evaluation information in the activity evaluation information database 114. If there is no activity evaluation information in the activity evaluation information database 114 (S203: N), the CPU 101 generates activity plan information input screen data in the RAM 102 (S204). In the analysis result transmission process (see FIG. 20), the CPU 101 transmits the activity plan information input screen data together with the analysis result output screen data to the respondent terminals 3a, 3b, 3c.

[0134] If the activity evaluation information database 114 contains activity evaluation information (S203: Y), the CPU 101 reads the activity evaluation information from the activity evaluation information database 114 (S205). When the CPU 101 reads the activity evaluation information (for example, see FIG. 10b), it generates activity evaluation information output screen data in the RAM 102 (S206). In the analysis result transmission process (see FIG. 20), the CPU 101 transmits the activity evaluation information output screen data together with the analysis result output screen data to the respondent terminals 3a, 3b, and 3c.

[0135] [Activity plan information input screen] FIG. 24 is a diagram showing the activity plan information input screen. After confirming the analysis results, the respondents A, B, and C of the group (sales) can devise a common activity plan for the group, and one person representing the group (e.g., respondent A) can input it as activity plan information. When the CPUs of the respondent terminals 3a, 3b, and 3c receive the activity plan information input screen data together with the analysis result output screen data from the main system 100, they store it in the RAM. Then, the analysis result output screen is displayed on the output section using the stored employee registration screen data. In this case, in addition to the line graph and score distribution, the transition column 24a displays the "Start" button 24b for displaying the activity plan information input screen. When the "Start" button 24b is selected by respondent A, the CPU of the respondent terminal 3a displays the activity plan information input screen 24c on the output section using the stored activity plan information input screen data.

[0136] When the respondent A inputs the activity plan information in the text input section 24d of the activity plan information input screen 24c, the CPU of the respondent terminal 3a stores the activity plan information in the RAM. When the respondent A selects, for example, the "Share Survey Results" button 24e, the CPU of the respondent terminal 3a transmits the activity plan information stored in the RAM to the main system 100. When the CPU 101 receives the activity plan information from the respondent terminal 3a, it registers it in the activity plan information database 113. In this example, the respondent A has set an activity plan that "checks each other's health conditions every Monday morning" that was devised by the respondents A, B, and C in the group (sales) (see Fig. 9(a) and Fig. 24). In this way, the respondent terminal is provided with an activity plan information receiving means that receives the activity plan proposed by the respondent as activity plan information. In this example, the respondent A inputs only one activity plan on behalf of the group, but one or more people may input one or more activity plans.

[0137] [Analysis result output screen] FIG. 25 is a diagram showing the analysis result output screen. After the respondents A, B, and C of the group (sales) answer the questionnaire and the evaluation of the activity plan, they can output the evaluation within the group as activity evaluation information for the activity plan that has already been devised by the group. When the CPU of the respondent terminal 3a, 3b, and 3c receives the activity evaluation information output screen data together with the analysis result output screen data from the main system 100, it stores it in the RAM. Then, it displays the analysis result output screen on the output section using the stored employee registration screen data. In this example, in addition to the line graph and score distribution, the transition column 25a displays the "2 wedo" button 25b for displaying the activity evaluation information output screen. When the "2 wedo" button 25b is selected by the respondent A, the CPU of the respondent terminal 3a displays the activity evaluation information output screen (FIG. 26) on the output section using the stored activity evaluation information output screen data.

[0138] [Activity evaluation information output screen] FIG. 26 is a diagram showing an activity evaluation information output screen. In this example, in addition to a line graph and a score distribution, the transition field 26a displays already-entered activity evaluation information as a review display field 26b. The review display field 26b displays an activity plan and its evaluation according to the activity evaluation information already registered in the activity evaluation information database 114. For example, as shown in FIG. 10(b), the activity evaluation information registered in the activity evaluation information database 114, for "discuss with everyone," has been highly rated by three people, so that as shown in the review display field 26b of FIG. 26, a smiley face mark 26c is lit and a comment field 26d is displayed indicating that there are no improvement plans.

[0139] On the other hand, as shown in FIG. 10(b), the activity evaluation information registered in the activity evaluation information database 114 has received low evaluations from three people for "reflecting," so as shown in the review display field 26b of FIG. 26, the troubled mark 26e is lit, and the fact that the improvement plan is 3 is displayed in the comment field 26f. When the respondent A selects the comment field 26f, the comments of each respondent are displayed (not shown). In this example, the comments displayed are, for example, "Shouldn't we propose a more specific reflection plan?", "Shouldn't we be more specific?", and "Shouldn't we all check the reflection?", which are registered in the improvement plan item in FIG. 10(b). In this way, in the second embodiment, the evaluation of the executed activity plan is displayed in an easy-to-understand manner. The evaluation of the activity plan is expressed by lighting the smiley face mark 26c and the troubled mark 26e, but the slider 26g between the smiley face mark 26c and the troubled mark 26e may be used to express whether there are more high evaluations or low evaluations.

[0140] [Second question selection process] Fig. 27 is a flowchart for explaining the procedure for the main system 100 to perform the second question selection process. The second question selection process executes a process for inputting an evaluation of an already inputted activity plan in addition to a process for selecting question information to be asked to each respondent. Note that the second question selection process is the above-mentioned question selection process (see Fig. 14) to which a certain process has been added. For this reason, only the process unique to the second question selection process, which is surrounded by a dashed line in Fig. 27, will be explained.

[0141] When the main system 100 finishes executing the multiple choice question machine learning process (S103), the CPU 101 judges whether or not there is an activity plan (S107). That is, the CPU 101 judges whether or not there is activity plan information in the activity plan information database 113. If there is activity plan information in the activity plan information database 113 (S107: Y), the CPU 101 reads the activity plan information from the activity plan information database 113 (S108). When the CPU 101 reads the activity plan information (for example, see FIG. 9(a)), it generates activity evaluation information input screen data in the RAM 102 (S109). After the activity evaluation information input screen generation (S109), if there is no activity evaluation information in the activity plan information database 113 (S107: N), the CPU 101 again monitors whether or not the date and time of the "distribution timing" has been reached (S101).

[0142] [Second questionnaire processing] Fig. 28 is a flow chart for explaining the procedure for the main system 100 to perform the second questionnaire process. The second questionnaire process is a process of sending different questions to each respondent and questions for evaluating the activity plan, and receiving answers for each respondent and answers evaluating the activity plan. Note that the second questionnaire process is the above-mentioned questionnaire process (see Fig. 16) to which a certain process has been added. Therefore, only the process unique to the second questionnaire process will be explained.

[0143] First, the CPU 101 generates question input screen data for each respondent in the RAM 102 from the selected question information generated in the RAM 102 in the selected question machine learning process (S103), and transmits it to each respondent terminal 3a, 3b, 3c together with the activity evaluation information input screen data generated in the RAM 102 in the second question selection process. When the CPUs of the respondent terminals 3a, 3b, 3c receive the question input screen data and the activity evaluation information input screen data for each respondent A, B, C from the main system 100, they store them in the RAM, and first display the question input screen for each respondent A, B, C on the output section using the question input screen data (see FIG. 17). When the respondent A, B, C answers all the questions and, for example, a confirmation button (not shown) is selected to check whether there are any input errors, the activity evaluation information input screen is then displayed on the output section using the activity evaluation information input screen data (see FIG. 29).

[0144] [Activity evaluation information input screen] 29 is a diagram showing an example of an activity evaluation information input screen. The activity evaluation information input screen is a page where respondents A, B, and C who executed an action plan input their evaluation in response to a question about the action plan that the main system 100 transmits to the respondent terminals 3a, 3b, and 3c. Depending on the content entered here, the answer information stored in the answer information database 112 is updated (see FIG. 10(a)).

[0145] FIG. 29(a) is a review selection screen where the respondent selects whether or not to review the activity plan. When the "Answer" button 29a on the review selection screen is selected by the respondents A, B, and C, the CPUs of the respondents A, B, and C output an evaluation input screen. FIG. 29(b) is an evaluation input screen where the respondent inputs an evaluation on the activity plan. The target activity plan information field 29b displays the activity plan of the activity plan information that is the subject of the review (see FIG. 9(a)). In this embodiment, the answer input field allows two ranks, for example, "Room for improvement!" and "It was good to do it!" to be input by selecting buttons 29c and 29d. However, the buttons 29c and 29d in the answer input field may have other names, and two or more ranks may be set.

[0146] Respondents A, B, and C select button 29c or 29d from the input section of respondent terminal 3a, 3b, and 3c. When respondents A, B, and C input their answers from the evaluation input screen, the CPU of respondent terminal 3a, 3b, and 3c stores the answers in RAM as activity evaluation information. The input answers are converted into numerical values ​​according to the conversion data stored in the storage device of respondent terminal 3a, 3b, and 3c depending on which button 29c or 29d was selected (e.g., button 29c=1, button 29d=0, etc.).

[0147] When the respondents A, B, C select the "Good job!" button 29c or the "Room for improvement!" button 29d on the evaluation input screen, the CPUs of the respondents A, B, C reflect this in the activity evaluation information and store it in the RAM. When the respondents A, B, C select the "Good job!" button 29c or the "Room for improvement!" button 29d on the evaluation input screen, the CPUs of the respondents A, B, C output an improvement proposal input screen. Figure 29(c) shows the improvement proposal input screen on which the respondent inputs improvement proposals for the activity plan. The improvement activity plan information column 29e displays the activity plan of the activity plan information that is determined to have room for improvement (see Figure 9(a)).

[0148] When respondents A, B, C input activity evaluation information into the text input section 24d of the activity plan information input screen 24c, the CPUs of the respondent terminals 3a, 3b, 3c reflect this in the activity evaluation information and store it in RAM. When respondents A, B, C select the "skip" button 29g without inputting activity evaluation information into the text input section 24d of the activity plan information input screen 24c, the CPUs of the respondent terminals 3a, 3b, 3c reflect this in the activity evaluation information and store it in RAM.

[0149] [Response information reception SC102] When the respondents A, B, and C answer all the questions, evaluate the questions that review the activity plan, and select a confirmation button (not shown) to check for input errors, for example, the CPU of the respondent terminal 3a, 3b, and 3c transmits the answer information and activity evaluation information stored in the RAM to the main system 100. When the CPU 101 receives the answer information for each of the respondents A, B, and C from the respondent terminal 3a, 3b, and 3c, it registers this in the answer information database 112 as shown in Figure 8(a) or Figure 8(b). In addition, when the CPU 101 receives activity evaluation information from the respondent terminal 3a, 3b, and 3c, it registers this in the activity evaluation information database 114 as shown in Figure 10(a).

[0150] For example, when respondent A selects the "I'm glad I did it!" button 29c, the CPU of the respondent terminal 3a stores "1" in the high rating of the activity evaluation information rating (1) and stores this in the RAM as the activity evaluation information. The CPU of the respondent terminal 3a then transmits the activity evaluation information stored in the RAM to the main system 100. When the CPU 101 receives the activity evaluation information from the respondent terminal 3a, it registers it in the activity evaluation information database 114 (see FIG. 10(a)).

[0151] For example, if respondent B selects the "Room for improvement!" button 29d and does not input an improvement plan proposal, the CPU of respondent terminal 3b stores "0" in the low evaluation of evaluation (2) of the activity evaluation information, and stores this in RAM as activity evaluation information. After that, the CPU of respondent terminal 3b transmits the activity evaluation information stored in RAM to the main system 100. When the CPU 101 receives the activity evaluation information from respondent terminal 3b, it registers it in the activity evaluation information database 114 (see FIG. 10(a)).

[0152] For example, when respondent C selects the "Room for improvement!" button 29d and inputs "Isn't it more essential to check not only health conditions but also engagement?" as an improvement plan proposal, the CPU of the respondent terminal 3c stores "0" in the low evaluation of the evaluation (3) of the activity evaluation information, stores "Isn't it more essential to check not only health conditions but also engagement?" as an improvement proposal of the evaluation (3) of the activity evaluation information, and stores this in the RAM as activity evaluation information. After that, the CPU of the respondent terminal 3c transmits the activity evaluation information stored in the RAM to the main system 100. When the CPU 101 receives the activity evaluation information from the respondent terminal 3c, it registers it in the activity evaluation information database 114 (see FIG. 10(a)).

[0153] When using the information analysis system W of the second embodiment of the present invention, the respondent can register an activity plan (action plan) after checking the analysis results of the department (sales) to which he / she belongs. When an activity plan (action plan) is registered, the respondent can evaluate the activity plan on the respondent terminal in addition to answering the multiple choice questions. The department (sales) to which the respondent belongs can collect candid opinions from the respondent, who is the person in the field who implemented the activity plan, and can look back on the respondent's confirmation of the effectiveness of the activity plan, and can use this as reference for the next activity plan (action plan) to be registered.

[0154] [Counting process] 30 is a flow chart for explaining the procedure of the tallying process performed by the main system 100. The tallying process is a process for tallying answer information and outputting the tallying result in response to a request from a person having a certain authority (e.g., the human resources person terminal 2, the respondent A). The CPU 101 monitors whether or not there is a tallying request from a person having a certain authority (e.g., the human resources person terminal 2, the respondent A) (S301). For example, when there is a tallying request from the human resources person terminal 2 (S301: Y), the CPU 101 transmits answer information from the answer information database 112 to the tallying system 200 and requests the tallying system 200 to execute tallying of the answer information (S302).

[0155] When the tallying system 200 finishes tallying the response information (S302), the CPU 101 generates tally result output screen data for displaying a tally result output screen in the RAM 102, and transmits this to the human resources personnel terminal 2. When the CPU of the human resources personnel terminal 2 receives the tally result output screen data, it stores it in the RAM and uses it to display the tally result output screen on the output section. The tally result output screen displays the tally results in tables, graphs, etc. for each respondent, question, organization, department of an organization, group, or respondent attribute. Note that in the tally results displayed on the tally result output screen, the names of the respondent are anonymous. When the tallying system 200 finishes processing, the CPU 101 monitors whether or not there is a tally request from a person with a certain authority (for example, the human resources personnel terminal 2, respondent A).

[0156] Third Embodiment In the first embodiment described above, what the respondent considers important among the nine key drivers is measured, and as shown in FIG. 22(a), a score for each predetermined element is calculated and output as the analysis result of the group, and as shown in FIG. 22(b), the analysis result of the group is output in a scatter diagram. By using the information analysis system W, the human resources officer H can extract the strengths and challenges of the group by checking the importance and scores of the group from elements such as "motivation," "sense of accomplishment," "opportunities for growth," "work load," "stress response," "job support," "satisfaction with evaluation," "challenge culture," "work environment," and "satisfaction with salary." On the other hand, in the third embodiment, the values ​​of each respondent in the group can be extracted.

[0157] FIG. 31 is a diagram showing an analysis result output screen for groups and individuals. FIG. 31(a) is a scatter diagram showing an analysis result output screen for the department (sales) to which respondents A, B, and C belong. CPU 101 displays scatter diagram 31a, which is a two-dimensional graph plotting the score of a specific group (sales) and the importance of a specific group (sales) on the axis according to the importance of respondents A, B, and C registered in the importance database 310. Scatter diagram 31a allows the organization to grasp the importance of each item for each employee of the organization collectively. For example, human resources officer H can judge whether or not the organization needs improvement by viewing scatter diagram 31a.

[0158] In this example, it can be seen from the scatter diagram 31a that the strengths of the specified group (sales) are "job / motivation" and "human relationships / relationship with superiors," which are plotted in the first quadrant with high importance and high score. Also, in this example, it can be seen from the scatter diagram 31a that the recommended improvement items for the specified group (sales) are "self-growth / sense of accomplishment" and "self-growth / opportunities for growth," which are plotted in the fourth quadrant with high importance and low score. In this way, for example, by viewing the scatter diagram 31a, the human resources officer H can see at a glance what items the specified group (sales) should work on to improve their motivation to contribute.

[0159] FIG. 31(b) is a diagram showing an analysis result output screen of respondent B (e.g., Suzuki Taro) who belongs to the department (sales). Table 31b is a diagram showing the analysis result of respondent B in numerical form. The first radar chart 31c is a diagram showing respondent B's attachment to the company he belongs to from the perspectives of "vision-oriented," "growth-oriented," "expectation-oriented," "self-control-oriented," and "team-oriented" in a radar chart. The second radar chart 31d is a diagram showing respondent B's vitality for the work content from the perspectives of "vision-oriented," "growth-oriented," "expectation-oriented," "self-control-oriented," and "team-oriented" in a radar chart. Table 31b, the first radar chart 31c, and the second radar chart 31d summarize the state of the items that each employee belonging to the organization considers important from five perspectives. For example, by viewing FIG. 31(b), the human resources manager H can confirm what factors are closely related to each employee's attachment to the organization, vitality to devote himself to work, etc.

[0160] In this example, the first radar chart 31c shows that the factors that influence whether respondent B can feel attached to the work content are "vision-oriented," "growth-oriented," and "team-oriented," which have values ​​of 50 or more. In other words, respondent B can feel attached to his organization if the concept and future image of wanting to grow and the like match in a specific group (sales), respondent B feels his own growth, and there is support from respondent A and respondent C. In addition, in this example, the second radar chart 31d shows that the factors that influence whether respondent B can feel energized in a specific group (sales) are "growth-oriented," "expectation-oriented," and "self-control-oriented." In other words, respondent B can feel energized to devote himself to his work when he feels his own growth in a specific group (sales), feels the expectations of respondent A and respondent C, and is in control of his own pace of work.

[0161] On the other hand, when the personnel manager H feels that the respondent B in a certain group (sales) is not immersed in his / her work, he / she can find out the reason why the respondent B is not immersed in his / her work by viewing the table 31b, the first radar chart 31c, and the second radar chart 31d. For example, when the personnel manager H views the second radar chart 31d and finds that the value of health thinking has decreased significantly, he / she can infer that the respondent B may not be able to work at his / her own pace. This allows the personnel manager H to check with the respondent A in the same group whether the respondent B is working at an excessive pace, or to take appropriate measures. In this way, for example, the personnel manager H can check the personality diagnosis and the values ​​of the specific respondent at a glance by viewing the table 31b, the first radar chart 31c, and the second radar chart 31d.

[0162] Although the embodiment of the present invention has been described above, the present invention is not limited to this embodiment, and can be modified within the scope of the object and purpose of the present invention. Although the present invention has been described as storing each piece of data in a table format, this data does not necessarily have to be expressed in a data structure based on a table. For example, the data may be expressed in a data structure such as a list, DB, queue, or other method.

[0163] In this example, the selected question information is predicted from the importance of one respondent and the amount of remaining information of one respondent through a selected question information model. However, the selected question information may be predicted from either the importance of one respondent or the amount of remaining information of one respondent through a selected question information model. In this example, the selected question information is predicted from the importance of one respondent and the amount of remaining information of one respondent through a selected question information model. However, the selected question information may be selected according to the frequency and / or item by updating the frequency of question information from the importance of one respondent and / or the amount of remaining information of one respondent. In this way, the main system 100 may be provided with a frequency update means for updating the frequency of question information.

[0164] In this example, the number of questions asked to the respondent is described as 16 or 32, but the number of questions need not be limited to this number. For example, the number of questions may be changed as appropriate by the person managing the information analysis system W or the human resources officer H using the information analysis system W. Furthermore, the human resources officer H using the information analysis system W may generate, update, and delete the respondent information as appropriate. Furthermore, the person managing the information analysis system W may generate, update, and delete the question information as appropriate. In this manner, the main system 100 may be provided with a storage means for updating the respondent information and the question information.

[0165] In this example, the "questions" prepared in the question information are intended to measure the psychological state of each respondent regarding their work and / or the organization, the willingness of each respondent to contribute to their work and / or the organization, the factors that each respondent considers important, etc. In particular, the purpose is to measure through the questionnaire what work engagement each respondent, which is a positive and fulfilling psychological state regarding work, is characterized by any one or a combination of vitality, enthusiasm, and absorption.

[0166] However, when the respondents are not employees but people belonging to a specific organization, the "questions" prepared in the question information may be intended to measure not only work-related matters but also the respondent's psychological state toward the organization, the respondent's willingness to contribute to the organization, the factors that each respondent considers important, etc.

[0167] In this example, the information analysis system W calculates scores for each element such as "motivation," "sense of accomplishment," "opportunities for growth," "workload," "stress response," "job support," "satisfaction with evaluation," "challenging culture," "workplace environment," and "satisfaction with salary," and aggregates the scores to derive what items the respondent considers important from nine key drivers and five perspectives, and estimates and notifies the respondent of his / her willingness to contribute to the organization. However, the elements for which the scores are calculated, the nine key drivers, and the five perspectives may be changed as appropriate. Furthermore, the scores may be calculated by a combination that is appropriate for statistical and interpretable properties in the context of the questionnaire, etc., without being limited to the elements for which the scores are calculated, the nine key drivers, and the five perspectives.

[0168] For example, "work enthusiasm" is associated with the key drivers "duty" and "personal growth" and a score is calculated. The items used here are grouped based on the results of statistical analysis and with interpretability taken into account. The method of aggregating items may be rearranged as appropriate, taking into account statistical validity and the validity of interpretation.

[0169] In this example, the first radar chart 31c and the second radar chart 31d show the analysis results from the viewpoints of "vision orientation", "growth orientation", "expectation orientation", "self-control orientation" and "team orientation", but these viewpoints may be increased or decreased, or the analysis results may be shown from other viewpoints. Furthermore, the wording may be changed as appropriate, such as changing "vision orientation" to "vision", "growth orientation" to "self-growth", "expectation orientation" etc. to "expectations of those around you", "self-control orientation" to "health orientation", and "team orientation" to "team (support)", etc. [Explanation of symbols]

[0170] 1 Information analysis device

Claims

1. a storage means for storing respondent information for identifying a respondent and question information for asking the respondent questions regarding duties, self-growth, health, human relations, support, recognition, philosophical strategy, organizational climate, and environment as factors that affect the willingness to contribute to the organization; an answer information receiving means for receiving, as answer information, a value of each element that affects the willingness to contribute, which is answered by a respondent in response to a question from a respondent terminal; an analysis means for performing an analysis according to a time series transition of past values ​​of each element from the answer information for each answerer received by the answer information receiving means, and generating an analysis result for each answerer; a contribution willingness estimation means for estimating a contribution willingness of each respondent to an organization for each element according to the analysis result obtained by the analysis means; an element estimation means for estimating which element is important for each respondent from among the willingness to contribute estimated by the willingness to contribute estimation means, and deriving the importance of each respondent from the response information to derive a causal relationship; a notification means for notifying a user of the willingness to contribute and the importance estimated by the willingness to contribute estimation means and the element estimation means; an importance model generating means for generating an estimation model by machine learning, using historical response information of all respondents as teacher data, and estimating parameters expressing a relationship between the response information of all respondents and the importance of all respondents using factor analysis or Bayesian statistics; an importance output means for outputting the importance of one respondent; The analysis means, the contribution willingness estimation means, and the element estimation means Using the estimation model, the response information of one respondent is analyzed, and the importance of the one respondent is estimated.

1. An information analysis device comprising:

2. 2. The information analysis device according to claim 1, a residual information amount model generation means for generating an estimation model by machine learning, which uses answer information of all respondents as teacher data, determines how long ago a question was selected from the answer information of all respondents and the answer information of each respondent, and estimates parameters expressing the relationship between the residual information amount, which is information for determining whether a question to be selected for a respondent is new or old, using factor analysis, Bayesian statistics, or clustering; a remaining information amount output means for outputting the remaining information amount of one respondent by analyzing the answer information of one respondent using the estimation model generated by the remaining information amount model generation means.

1. An information analysis device comprising:

3. 3. The information analysis device according to claim 2, Using the importance of all respondents and / or the amount of remaining information of all respondents as training data, a selection question model generating means for generating an estimation model by machine learning to estimate a parameter expressing a relationship between the importance of all respondents and / or the amount of remaining information of all respondents and the selection question information of all respondents; and a selective question output means for outputting selective question information to one answerer by analyzing the importance of one answerer and / or the amount of remaining information of one answerer using the estimation model generated by the selective question model generation means.

1. An information analysis device comprising:

4. The information analysis device according to any one of claims 1 to 3, The notification means notifies the importance of one answerer, all answerers, each question, each organization, each department of an organization, each group, or each attribute of an answerer, or a combination of these.

1. An information analysis device comprising:

5. 5. The information analysis device according to claim 4, The element estimation means estimates values ​​that one respondent or all respondents consider important from among vision orientation, growth orientation, expectation orientation, self-control orientation, and team orientation, The notification means notifies the value estimated by the element estimation means.

1. An information analysis device comprising:

6. 6. The information analysis device according to claim 5, The element estimation means estimates the value from the respondent's attachment to an organization and the respondent's vitality in the organization, The notifying means notifies the value estimated by the element estimating means by dividing it into the attachment and the vitality.

1. An information analysis device comprising:

7. 7. The information analysis device according to claim 5 or 6, The notification means notifies the importance in the form of a graph.

1. An information analysis device comprising:

8. a storing step of storing respondent information for identifying the respondent and question information for asking the respondent questions regarding duties, self-growth, health, human relations, support, recognition, philosophy strategy, organizational culture, and environment as factors that affect the willingness to contribute to the organization; an answer information receiving step of receiving, as answer information, a value for each factor that affects the willingness to contribute, which is answered by a respondent in response to a question from the respondent terminal; an analysis step of performing an analysis according to a time series transition of past values ​​of each element from the answer information for each answerer received in the answer information receiving step, and generating an analysis result for each answerer; a willingness to contribute estimation step of estimating the willingness to contribute to the organization for each respondent for each element according to the analysis result obtained by the analysis step; an element estimation step of estimating which element is important for each respondent from the willingness to contribute estimated in the willingness to contribute estimation step, and estimating the importance of each respondent by deriving a causal relationship from the response information; a notification step of notifying the willingness to contribute and the importance estimated by the willingness to contribute estimation step and the element estimation step; an importance model generation process for generating an estimation model by machine learning, using historical response information of all respondents as teacher data, and estimating parameters expressing the relationship between the response information of all respondents and the importance of all respondents using factor analysis or Bayesian statistics; and an importance output step of outputting the importance of one respondent, Through the analysis step, the contribution willingness estimation step, and the element estimation step, Using the estimation model, the response information of one respondent is analyzed, and the importance of the one respondent is estimated. The information analysis method according to the present invention is characterized in that the above-mentioned steps are implemented by a computer.

9. A storage function for storing respondent information for identifying the respondent and question information for asking the respondent questions about duties, self-growth, health, human relations, support, recognition, philosophy strategy, organizational culture, and environment as factors that affect the willingness to contribute to the organization; an answer information receiving function that receives, as answer information, values ​​of each factor that affects the willingness to contribute, which is answered by the respondent in response to the question from the respondent's terminal; an analysis function that performs an analysis according to a time series transition of past values ​​of each element from the answer information for each respondent received by the answer information receiving function, and generates an analysis result for each respondent; a contribution willingness estimation function that estimates the contribution willingness of each respondent to the organization for each element according to the analysis result obtained by the analysis function; An element estimation function that estimates which element is important for each respondent from the willingness to contribute estimated by the willingness to contribute estimation function, and estimates the importance of each respondent by deriving a causal relationship from the response information; A notification function for notifying the willingness to contribute and the importance estimated by the willingness to contribute estimation function and the element estimation function; an importance model generation function that uses the historical response information of all respondents as teacher data to generate an estimation model by machine learning, which estimates parameters expressing the relationship between the response information of all respondents and the importance of all respondents using factor analysis or Bayesian statistics; an importance output function for outputting the importance of one respondent; The analysis function, the contribution motivation estimation function, and the element estimation function are used to Using the estimation model, the response information of one respondent is analyzed, and the importance of the one respondent is estimated. An information analysis program that enables a computer to do this.

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