JP2011008440A - Human resource management support device, method, and computer program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To flexibly set knowledge necessary for the selection of a person in each role in an organization activity, and to collect information as to whether each person has such knowledge.SOLUTION: This human resource management support device totals the number of electronic mails the text of each of which includes a keyword representing knowledge necessary for a project to be newly planned for each person set as a sender, and when the number is equal to or more than a threshold, the human resource management support device determines that the person has the knowledge, and displays the knowledge each person has, or displays the person who has each knowledge on a knowledge map indicating a correlation of knowledge necessary for the project. Also, the human resource management support device selects the person who has the knowledge necessary for each role of the project, and forms a project team.

Description

  The present invention relates to a human resource management support apparatus and method that supports search of human resources by collecting information on what knowledge each person has, and a computer program.

  When business or the like is performed in an organization such as a company, various kinds of knowledge are required according to each role in the business. Therefore, it is important to determine the person who is engaged in organizational activities such as business based on what kind of knowledge each person has. For example, a manager who organizes a new organizational activity lists knowledge necessary for this organizational activity, and then investigates the presence or absence of the knowledge for all the candidate members, and determines a member. At this time, the investigation of the presence / absence of knowledge utilized contents taken into consideration when organizing past organizational activities, information on current and past work in charge, and the like.

  On the other hand, Patent Document 1 describes a technique for acquiring human skills such as interpersonal ability, leadership, and management ability from exchanged emails. In this technology, the range of organizations with which communication is performed is determined by determining the organization from the domain included in the email addresses of emails sent and received by each person, and human skills are calculated from that range. Judge that human skill is high. Then, a human resource is searched based on the expertise of the human resource and the calculated human skill, and the search result is output.

JP 2007-286666 A

  As mentioned above, if you want to investigate the presence or absence of knowledge necessary for new organizational activities based on the content considered when organizing past organizational activities, you will not necessarily obtain information on the knowledge necessary for new organizational activities. There may not be. For example, the knowledge necessary for new organizational activities is “SQL”, “Network”, etc., and the knowledge considered in the organizational activities that have occurred so far is “English ability”, “Acoustic engineering”, “Rakuten”. ..., The content considered in the past organizational activity membership may not be available.

On the other hand, it is not easy to grasp what kind of knowledge each person has from his / her work. For example, even if Mr. X is engaged in work mainly using the knowledge of “SQL” and “C ++”, and Mr. Y is engaged in work mainly using the knowledge of “basic examination”, Mr. Y may be familiar with “SQL” and “P2P”, and may be familiar with “basic study” and “performance evaluation”. Even if they are engaged in the same business, their roles may be subdivided. In this way, the assigned job does not necessarily represent the knowledge of the person engaged in the job, and it is difficult to extract hidden knowledge.
Therefore, the manager had to investigate each person's past achievements, etc., and determine whether he had the knowledge necessary for a new organizational activity. Since this is a cumbersome task that requires time and human costs, there is a demand for easily collecting information on whether or not each person has the knowledge necessary for the selection of each role in organizational activities. there were.

Furthermore, business has diversified in recent years, and there are many companies that want to work effectively by launching projects in ad hoc and flexibly allocating human resources. For example, there is a need to enable flexible and flexible resource allocation beyond existing teams and development lines. Against this background, it is required to collect information as to whether or not each person has knowledge necessary for selecting a role for each role in organizational activities more quickly and easily.
However, Patent Document 1 described above does not solve such a problem.

  The present invention has been made to solve the above-mentioned problems, and its purpose is to flexibly set knowledge necessary for selection of each role in organizational activities and collect whether each person has that knowledge or not. It is an object to provide a human resource management support apparatus and method, and a computer program.

  In order to solve the above-described problems, the present invention stores a mail data storage unit that stores mail data, a knowledge map storage unit that stores keywords representing knowledge, and person knowledge data in which person identification information and knowledge are associated with each other. A person knowledge storage unit, a mail reading unit for reading the mail data from the mail data storage unit, an important keyword list reading unit for reading the keywords from the knowledge map storage unit, and the mail data read by the mail reading unit The number of the mail data in which the keyword read by the important keyword list reading unit appears in the mail body is counted for each person set as the sender in the mail data, and each person is set as the sender. Of the mail data compiled for the mail data A person knowledge extracting unit that extracts a keyword having a threshold equal to or greater than a threshold as knowledge possessed by each person, person specifying information for identifying each person, and the knowledge extracted by the person knowledge extracting unit for each person And a person knowledge output unit that writes the person knowledge data associated with the information into the person knowledge storage unit.

  The present invention is also the human resource management support apparatus described above, wherein a word having a predetermined part of speech is selected from a mail text indicated by the mail data read by the mail reading unit, and a word determined in advance from the selected word Keywords are extracted, the number of the extracted keywords appearing in the email body is totaled, an important keyword extraction unit that selects keywords whose total number of occurrences is equal to or greater than a threshold, and selected by the important keyword extraction unit And an important keyword list output unit for writing the keyword into the knowledge map storage unit.

  The present invention is also the human resource management support apparatus described above, wherein the knowledge map storage unit further stores knowledge map data including a keyword representing knowledge necessary for each project, and the knowledge map data is stored in the knowledge map. A knowledge map reading unit to be read from the storage unit, a person knowledge reading unit to read the person knowledge data from the person knowledge storage unit based on designated person specifying information, and the knowledge map data to be read by the knowledge map reading unit The keyword included in the knowledge map data is displayed, and the keyword representing the knowledge included in the person knowledge data read by the person knowledge reading unit is identified among the keywords included in the knowledge map data. Na Further comprising a knowledge map display unit for displaying information indicating that it has as Tsu di, characterized in that.

Further, the present invention is the human resource management support apparatus described above, wherein the knowledge map storage unit further stores knowledge map data including a keyword representing a knowledge necessary for each project, and the knowledge map data is stored in the knowledge map storage. A knowledge map reading unit that is read from a person, a person knowledge reading unit that reads the person knowledge data from the person knowledge storage unit, a keyword included in the knowledge map data read by the knowledge map reading unit, and the person knowledge The person specified by the person specifying information in the selected person knowledge data corresponding to the keyword is selected from the person knowledge data read by the reading unit, and the person knowledge data including the knowledge corresponding to the keyword is selected. Further comprising, it is characterized by a knowledge map display unit for displaying the information.
In the human resource management support apparatus described above, the knowledge map display unit totals the number of the person knowledge data including the knowledge corresponding to the keyword for each keyword included in the knowledge map, and totals the keyword. You may make it display the number which carried out.

Further, the present invention is the human resource management support apparatus described above, from the candidate knowledge reading unit that reads the person knowledge data from the person knowledge storage unit, and the person knowledge data read by the candidate knowledge reading unit, Team organization for identifying person knowledge data including knowledge required for job title, and team organization for displaying information on the person identified by the person identification information in the person knowledge data identified by the team organization unit And a result display unit.
In the human resource management support apparatus described above, the team organization unit may specify the person knowledge data in the order in which many knowledges required for the post are included.
Further, in the human resource management support apparatus described above, the team organizing unit identifies person knowledge data including most knowledge except for the knowledge in the person knowledge data identified from knowledge required for the post. You may make it repeat the process to perform.

  The present invention is the above-described human resource management support apparatus, further comprising a person information storage unit that stores operating points associated with each person specifying information, and the team organization unit is read by the candidate knowledge reading unit. Identifying the person knowledge data including the person specifying information associated with the operating points more than the consumption operating points necessary for the position and the knowledge required for the position among the acquired person knowledge data. Features.

  The present invention also includes a mail data storage unit for storing mail data, a knowledge map storage unit for storing keywords representing knowledge, and a person knowledge storage unit for storing person knowledge data associated with person identification information and knowledge. A human resource management support method used in a human resource management support apparatus comprising: a mail reading unit that reads the mail data from the mail data storage unit; and an important keyword list reading unit that is from the knowledge map storage unit. An important keyword list reading process for reading the keywords, and a person knowledge extraction unit, wherein the keywords read in the important keyword list reading process among the mail data read in the mail reading process appear in the mail text. Number Aggregate for each person who is set as the sender in the mail data, and extract a keyword with the number of the mail data totaled for the mail data with each person as the sender as a knowledge held by each person A person knowledge extraction process, and a person knowledge output unit corresponding to the person knowledge data that associates the person identification information for identifying each person with the knowledge extracted in the person knowledge extraction process for each person. It is a human resource management support method characterized by comprising a human knowledge output process for writing to a storage unit.

  Further, the present invention relates to a computer used as a human resources management support apparatus, a mail data storage unit for storing mail data, a knowledge map storage unit for storing keywords representing knowledge, person knowledge data associated with person identification information and knowledge. A person knowledge storage unit for storing; a mail reading unit for reading the mail data from the mail data storage unit; an important keyword list reading unit for reading the keywords from the knowledge map storage unit; and the mail data read by the mail reading unit Of these, the number of the mail data in which the keyword read by the important keyword list reading unit appears in the mail body is totaled for each person set as the sender in the mail data, and the mail with each person as the sender About data A person knowledge extracting unit that extracts a keyword whose total number of mail data is equal to or greater than a threshold as knowledge possessed by each person, person specifying information for identifying each person, and the person knowledge extracting unit for each person This is a computer program that functions as a person knowledge output unit that writes person knowledge data associated with the knowledge extracted by the person knowledge storage unit.

According to the present invention, information on whether or not each person has knowledge that the user has freely selected as necessary in a new organizational activity can be flexibly obtained based on the past e-mail of each person. Can be collected. That is, it is possible to collect information about whether or not each person has the knowledge of different knowledge using the same stored e-mail. In addition, keywords that are considered to represent knowledge can be extracted from the stored mail. Then, based on the extracted knowledge information of each person, it is possible to display knowledge possessed by a specific person or display a person having specific knowledge.
Furthermore, it is possible to automatically select a person who is engaged in organizational activities based on the collected information. At this time, it is possible to select a person in consideration of the operation of each role in the organizational activity and the overall operation balance of each person.

It is a figure which shows the operation | movement outline | summary of the human resource management assistance apparatus by 1st embodiment of this invention. It is a block diagram of the human resource management assistance apparatus by the embodiment. It is a figure which shows the data structural example of the mail data by the same embodiment. It is a figure which shows the data structural example of the process mail data by the same embodiment. It is a figure showing an example of data composition of an important keyword list by the embodiment. It is a figure which shows the example of a data structure of the knowledge map data by the embodiment. It is a figure which shows the data structural example of the person knowledge data by the embodiment. It is a figure which shows the example of a data structure of the person information data by the embodiment. It is a figure which shows the processing flow of the important keyword list production | generation process by the embodiment. It is a figure showing an example of important keyword list generation processing by the embodiment. It is a figure which shows the processing flow of the knowledge extraction process by the embodiment. It is a figure showing an example of knowledge extraction processing by the embodiment. It is a figure which shows the processing flow of the personal knowledge display process by the embodiment. It is a figure which shows the example of a screen display of the personal knowledge by the embodiment. It is a figure which shows the processing flow of the team knowledge display process by the embodiment. It is a figure which shows the example of a screen display of the team knowledge by the embodiment. It is a figure which shows the processing flow of the processing flow of the team organization process by the embodiment. It is a figure which shows the processing flow of the person selection process by the personal comprehensiveness importance algorithm by the embodiment. It is a figure which shows the processing flow of the person selection process by the group comprehensive importance importance algorithm by the embodiment. It is a figure which shows the operation example of the team organization of the human resource management assistance apparatus by the embodiment. It is a figure which shows the operation example of the team organization of the human resource management assistance apparatus by the embodiment.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.
FIG. 1 is a diagram showing an outline of operation of a human resource management support apparatus according to an embodiment of the present invention. In the following, a case where a company as an organization forms a new project team will be described as an example.

  In the figure, first, the human resources management support device describes the knowledge (hereinafter referred to as “knowledge”) required for the newly organized project from the accumulated past e-mail (hereinafter simply referred to as “mail”). ) For example, the knowledge information about which knowledge each person has among the knowledge such as “SQL”, “basic examination”, “server”, and “network” is collected. The human resource management support apparatus displays a person having each knowledge based on the collected person knowledge information on a knowledge map generated by a user such as a manager who organizes a project team. A knowledge map is a distribution map showing the knowledge necessary for a project team and the correlation between those knowledge. For example, in the same figure, when Mr. A and Mr. B are selected as project team candidates, Mr. A is associated with the knowledge “Linxx”, “Windxxx”, “SQL”, and “JAxx” that A has. “B” is displayed in association with the knowledge “Linxx”, “Windxxx”, “basic study”, and “performance evaluation” possessed by Mr. B. As a result, when these two members are selected as members of the project team, they have strong knowledge as a team, that is, knowledge that they have in common, and knowledge that is necessary for development but weak as a team, that is, they do not have both Knowledge can be shown visually.

Furthermore, the human resources management support device selects a person based on the collected person knowledge information so that the knowledge of the person engaged in each role of the project team satisfies the knowledge necessary for each role in the project team, and the project Organize a team. For example, the human resources management support apparatus selects a person who has the most knowledge required for a management position as a leader, and then selects a person who has the next most necessary knowledge for the management position as a sub-leader. Select as. Furthermore, the human resource management support apparatus selects a person who is suitable for the managerial position. At this time, the person who has the most knowledge required for the managerial position is selected as the first member, and the second and subsequent persons are already members of the managerial position from the knowledge required for the managerial position. The person who has the most knowledge except the knowledge of the person selected as is selected.
In addition, the selection of personnel taking into account the operation of each role in the project team and the operation of each person in the entire project currently being executed by the entire company is performed.

  FIG. 2 is a configuration diagram of the human resource management support apparatus 1 according to the embodiment of the present invention, and only functional blocks related to the present invention are extracted and shown. In the figure, the human resource management support device 1 can be realized by a computer device such as one or a plurality of servers, such as a mail data storage unit 11, a processed mail data storage unit 12, a knowledge map storage unit 13, a person knowledge. A storage unit 14, a person information storage unit 15, an important keyword generation unit 20, a person knowledge processing unit 30, a knowledge map display processing unit 40, a team organization result display processing unit 50, and a team organization processing unit 60 are configured. . The knowledge map display processing unit 40 and the team organization result display processing unit 50 are connected to a user terminal 100 which is a computer terminal such as a personal computer via a network or the like.

  The mail data storage unit 11, the processed mail data storage unit 12, the knowledge map storage unit 13, the person knowledge storage unit 14, and the person information storage unit 15 are realized by a hard disk device, a semiconductor memory, or the like, for example, using a database server. be able to. The mail data storage unit 11 stores mail data that is data of transmitted and received e-mails. The processed mail data storage unit 12 stores processed mail data in which the text of the mail data is replaced with a keyword included in the text. The knowledge map storage unit 13 stores knowledge map data indicating the correlation of knowledge necessary for a project and important keywords that are words representing the knowledge. The person knowledge storage unit 14 stores the knowledge possessed by each person. The person information storage unit 15 stores an operation point that is a numerical value indicating how much each person can operate.

The important keyword generation unit 20 includes a mail reading unit 21, an important keyword extraction unit 22, a processed mail data output unit 23, and an important keyword list output unit 24.
The mail reading unit 21 reads mail data from the mail data storage unit 11. The important keyword extraction unit 22 extracts an important keyword, that is, a keyword representing knowledge based on the mail text of each mail data. The processed mail data output unit 23 generates processed mail data in which the text of the mail data is replaced with a keyword included in the text, and writes the processed mail data in the processed mail data storage unit 12. The important keyword list output unit 24 writes the important keywords extracted by the important keyword extraction unit 22 in the knowledge map storage unit 13.

The person knowledge processing unit 30 includes an important keyword list reading unit 31, a processed mail data reading unit 32, a person knowledge extraction unit 33, and a person knowledge output unit 34.
The important keyword list reading unit 31 reads an important keyword list from the knowledge map storage unit 13. The processed mail data reading unit 32 reads processed mail data from the processed mail data storage unit 12. The person knowledge extracting unit 33 counts the number of processed mail data in which important keywords appear as the number of appearing mails for each person who is the sender of the mail, and among the important keywords calculated for each of the senders, the number of appearing mails is a condition. More important keywords are extracted as knowledge of the person. The person knowledge output unit 34 writes person knowledge data indicating the knowledge extracted for each person in the person knowledge storage unit 14.

The knowledge map display processing unit 40 includes a knowledge map creation unit 41, a knowledge map reading unit 42, a knowledge map display unit 43, a person selection input unit 44, and a person knowledge reading unit 45.
The knowledge map creation unit 41 receives knowledge correlation information necessary for the project from the user terminal 100, generates knowledge map data, and writes the knowledge map data in the knowledge map storage unit 13. The knowledge map reading unit 42 reads knowledge map data from the knowledge map storage unit 13. The person selection input unit 44 receives, from the user terminal 100, selection information of a person whose knowledge is to be displayed on the knowledge map. The person knowledge reading unit 45 reads the person knowledge data of the selected person from the person knowledge storage unit 14. The knowledge map display unit 43 causes the user terminal 100 to display a knowledge map based on the knowledge map data, or to display the knowledge of the selected person or the person having the selected knowledge based on the person knowledge data. Or

  The team organization result display processing unit 50 includes a team organization condition input unit 51 and a team organization result display unit 52. The team organization condition input unit 51 receives project team organization conditions from the user terminal 100. The team organization result display unit 52 causes the user terminal 100 to display information on the person selected for each role of the project in the team organization processing unit 60.

The team organization processing unit 60 includes a team organization condition receiving unit 61, a candidate knowledge reading unit 62, a team organization unit 63, a team organization result output unit 64, an operating point update unit 65, and a person data reading unit 66.
The team organization condition receiving unit 61 receives the team organization condition received by the team organization condition input unit 51 from the user terminal 100. The candidate knowledge reading unit 62 reads person knowledge data of a person who is a candidate for a project member from the person knowledge storage unit 14. The person data reading unit 66 reads person information data of a person who is a member candidate from the person information storage unit 15. The team organization unit 63 selects a person for each role in the project based on the team organization conditions, the person knowledge data, and the person information data. The team organization result output unit 64 outputs information on the person selected for each role of the project to the team organization result display processing unit 50. The operation point update unit 65 updates the current operation point of the person selected as each role to a point obtained by subtracting the consumption operation points obtained by quantifying the operation required for the selected role.

  FIG. 3 is a diagram illustrating a data configuration example of mail data stored in the mail data storage unit 11. In the figure, the mail data includes a mail ID that is identification information for uniquely identifying the mail, a sender ID that is identification information for uniquely identifying the sender of the mail, the date and time when the mail was transmitted, and text data. Contains information such as the file name of the file. As the sender ID, for example, the sender's mail address can be used, and the sender ID is used as a person ID for uniquely identifying a person. The body data is text data of the mail body.

  FIG. 4 is a diagram illustrating a data configuration example of the processed mail data stored in the processed mail data storage unit 12. In the drawing, the processed mail data includes a mail ID, a sender ID, a date and time, and a keyword extracted from the mail text.

  FIG. 5 is a diagram illustrating a data configuration example of the important keyword list stored in the knowledge map storage unit 13. In the figure, the important keyword list is made up of a plurality of records in which a keyword selected as an important keyword is associated with a keyword ID that uniquely identifies the keyword.

  FIG. 6 is a diagram illustrating a data configuration example of knowledge map data stored in the knowledge map storage unit 13. In the figure, the knowledge map data is composed of a knowledge map ID for uniquely identifying the knowledge map data and a knowledge list. As the knowledge map ID, for example, a knowledge map, a project name, or the like can be used. Further, the knowledge list includes a plurality of keywords associated with a keyword indicating the knowledge constituting the knowledge map, a keyword ID that uniquely identifies the keyword, and a parent keyword ID that is a keyword ID corresponding to the knowledge that is higher in the knowledge. Consists of records.

  FIG. 7 is a diagram illustrating a data configuration example of person knowledge data stored in the person knowledge storage unit 14. In the drawing, the person knowledge data includes a person ID, a keyword ID, the number of appearances, and a knowledge flag. The number of appearances indicates the number of mails in which the keyword specified by the keyword ID appears among the mails transmitted by the person specified by the person ID. The knowledge flag indicates whether or not the keyword specified by the keyword ID is the knowledge of the person specified by the person ID, and is set when there is knowledge, and is reset when there is no knowledge. It should be noted that the total number of keywords that appear in the mail text may be used as the number of appearances.

  FIG. 8 is a diagram illustrating a data configuration example of the person information data stored in the person information storage unit 15. In the figure, the person information data includes a record in which a person ID, the name of the person specified by the person ID, and the remaining operating points of the person are associated with each other. In addition to the name, personal information of the person may include information such as the department to which the user belongs and contact information.

FIG. 9 is a diagram illustrating a processing flow of an important keyword list generation process by the important keyword generation unit 20.
First, the important keyword extraction unit 22 of the important keyword generation unit 20 reads an excluded word list stored in advance in a storage unit provided in itself or in a storage unit such as the processed mail data storage unit 12 (step S105). The excluded word list is a list of words that are not appropriate to be used as knowledge, and includes, for example, words used in expressions specific to e-mail and words used in any business or field. For example, the word “care” used in general email greetings such as “Thank you for your support. This is XX for the development department”, the word “development department” for organization, etc. For example, a word to be used, for example, “meeting” can be excluded.

  Subsequently, the mail reading unit 21 reads one piece of mail data out of the mail data that has not been read from the mail data storage unit 11, and is further indicated by the file name of the body data described in the mail data. The mail text is read from the file (step S110). The important keyword extraction unit 22 deletes a quoted part from another mail from the mail text read in step S110 (step S115). Specifically, it deletes the line where a predetermined character or symbol indicating that it is a quote from another mail is described at the beginning of the line, or describes that the following is a quoted part from another mail Delete the line after the current line.

  When the important keyword extraction unit 22 performs morphological analysis on the mail body from which the quoted part is deleted in step S115 (step S120), a word having a predetermined part of speech, for example, a word whose part of speech is a noun is extracted from the result of the morphological analysis ( Step S125). Subsequently, the important keyword extraction unit 22 deletes words included in the excluded word list read in step S105 from the words extracted in step S125 (step S130).

Subsequently, the important keyword extraction unit 22 acquires keywords from the words obtained as a result of step S130 as follows. That is, first, in step S130, the important keyword extracting unit 22 stores each word remaining as a result of deleting the word included in the excluded word list in its own storage unit or a storage unit such as the processed mail data storage unit 12 or the like. It is determined whether it is registered in the keyword conversion data stored in. The keyword conversion data is data for converting different words having the same meaning into the same keyword, and indicates correspondence between words and keywords. For example, “network”, “network”, and “NW” have the same meaning, and these words use “network” as a keyword. In this case, the keyword “network” is registered in the keyword conversion data in association with the words “network” and “NW”. When the word obtained as a result of step S130 is registered in the keyword conversion data, the important keyword extraction unit 22 reads the keyword registered in the keyword conversion data in association with the word and replaces it with the word. Words that are not registered in the keyword conversion data are directly used as keywords.
Thus, when keywords are acquired from all the words obtained as a result of step S130, the processed mail data output unit 23 acquires the acquired keywords, the mail ID, the sender ID, and the mail ID acquired from the mail data read in step S110. The processed mail data set with the date and time is created, and the created processed mail data is written into the processed mail data storage unit 12 (step S135). At this time, if there is a duplication in the acquired keyword, the duplication is eliminated and it is written in the processed mail data.

  The important keyword extraction unit 22 counts the number of appearances of each keyword obtained in step S135 (step S140). Specifically, first, the important keyword extraction unit 22 counts the number of appearances of each keyword obtained in step S130. For example, if the words “network” and “NW” are converted to the keyword “network” in step S130, the number of occurrences of the word “network” is 1 and the number of occurrences of the word “NW” is 2, the keyword “network” The number of occurrences of “network” “3 times” is counted. Subsequently, when one of the keywords for which the number of appearances is counted is selected, it is determined whether or not the keyword is registered in the keyword appearance number list stored in the storage unit included in the important keyword extraction unit 22. If not registered, the selected keyword is associated with the counted number of appearances and added to the keyword appearance number list. On the other hand, if already registered, the counted number of appearances is added to the number of appearances stored in association with the selected keyword. The important keyword extraction unit 22 selects the next keyword and performs the same processing until all the keywords obtained in step S135 are selected.

  The mail reading unit 21 determines whether or not all mail data has been read from the mail data storage unit 11, and determines that there is mail data that has not been read yet (step S145: NO), the processing from step S110 is performed. repeat. On the other hand, if it is determined that all mail data has been read (step S145: YES), the important keyword list output unit 24 extracts a predetermined number n of keywords as important keywords from the keyword appearance number list in descending order of appearance numbers. (Step S150). A keyword having the number of appearances equal to or greater than a predetermined threshold may be an important keyword. In this case, the threshold may be determined based on a predetermined ratio with respect to the number of mail data. When the keyword ID is assigned to each keyword extracted as an important keyword, the important keyword list output unit 24 generates an important keyword list including records in which the keyword extracted as the important keyword is associated with the keyword ID of the keyword. Then, it is written in the knowledge map storage unit 13 (step S155).

  FIG. 10 shows an example of the important keyword list generation process shown in FIG. 9 described above, and only a part of the data is described. The read mail data D11 indicates the mail data and the mail text read by the mail reading unit 21 from the mail data storage unit 11 (step S110). The important keyword extraction unit 22 deletes the quoted portion in which “>” is written at the head of the line from the mail body of the read mail data D11 (step S115), and then performs the morphological analysis to analyze the mail being analyzed. Data D12 is obtained (step S120). In the analyzed mail data D12, the result of the morphological analysis is “the other day / of / consultation / of / case / in”... “Defect / of / cause / has / SQL / according / accessed / of / in / "..." indicates that it was obtained.

  Subsequently, the important keyword extraction unit 22 extracts nouns from the morphological analysis result indicated in the mail data D12 being analyzed (step S125), and deletes excluded words such as “the other day”, “meeting”, and “case”. This is performed (step S130). The keywords “defect”, “cause”, “SQL”, “access”,... Are obtained from the words obtained as a result. The processed mail data output unit 23 generates processed mail data D13 in which the obtained keyword and the mail ID, sender ID, and date / time of the mail data D11 are set, and writes the processed mail data D13 in the processed mail data storage unit 12 (step S135).

  When the number of keyword appearances is totaled for the mail for all members by the important keyword extraction unit 22 (steps S140 and S145), a keyword appearance number list D14 is obtained. The important keyword list output unit 24 generates an important keyword list D15 from the result of extracting the keywords having the top n occurrences from the keyword appearance number list D14 as important keywords (step S150), and writes them to the knowledge map storage unit 13 (step S150). Step S155). In the figure, it is shown that an important keyword list D15 including “JAxx”, “basic examination”, “SQL”, “network”, “performance evaluation”, “C ++”,.

  In the above, all the mail data stored in the mail data storage unit 11 is read, but only the mail data in which the date and time of a predetermined period such as the past year is set in the date and time information. It may be read.

  In the above, the number of keywords appearing in each mail is counted and totaled, but the number of mails in which each keyword appears may be counted. In this case, if there are duplicate keywords among the acquired keywords in step S140 in FIG. 9, only one of them is left. When one of the keywords is selected, it is determined whether or not the keyword is registered in the keyword appearance number list stored in the storage means included in the important keyword extraction unit 22. If not registered, the selected keyword is associated with the appearance number “1” and added to the keyword appearance number list. On the other hand, if already registered, 1 is added to the number of appearances stored in association with the selected keyword.

FIG. 11 is a diagram illustrating a processing flow of knowledge extraction processing by the person knowledge processing unit 30.
First, the important keyword list reading unit 31 of the person knowledge processing unit 30 reads the important keyword list from the knowledge map storage unit 13 (step S205). Subsequently, the processed mail data reading unit 32 reads one processed mail data out of the processed mail data not yet read from the processed mail data storage unit 12 (step S210). The person knowledge extracting unit 33 leaves only keywords that match any one of the keywords described in the important keyword list read in step S205 from the keywords described in the processed mail data read in step S210, and which keyword The keywords that do not match both are deleted (step S215).

The person knowledge extraction unit 33 updates the number of mails in which the keyword extracted in step S215 appears in the same sender mail (step S220).
Specifically, first, when the person knowledge extracting unit 33 acquires the sender ID described in the processed mail data, the person knowledge extracting unit 33 obtains the person knowledge data in the person knowledge storage unit 14 in which the sender ID is set as the person ID. Search for.
If there is no person knowledge data in which the acquired sender ID is set as the person ID, the person ID set with the acquired sender ID, the keyword ID read from the important keyword list corresponding to the keyword extracted in step S215, and The person knowledge data set with the appearance number “1” is generated and written into the person knowledge storage unit 14.
On the other hand, when the person knowledge data is specified, one keyword extracted in step S215 is selected, and the keyword ID read from the important keyword list corresponding to the selected keyword is registered in the specified person knowledge data. Determine whether or not. If not registered, the keyword ID of the selected keyword and the number of appearances “1” are associated with each other and added to the person knowledge data. If registered, 1 is added to the number of appearances set in the person knowledge data in association with the keyword ID of the selected keyword. Then, the next keyword is selected and the same processing is performed until all the extracted keywords are selected in step S215.

  The person knowledge extracting unit 33 determines whether or not all processed mail data has been read from the processed mail data storage unit 12, and if it is determined that there is processed mail data that has not yet been read (step S225: NO), step S210. Repeat the process from. On the other hand, if it is determined that all processed mail data has been read (step S225: YES), the person knowledge output unit 34 selects and reads one person knowledge data stored in the person knowledge storage unit 14 (step S225). S230).

  The person knowledge output unit 34 selects one keyword ID that has not yet been selected from the keyword IDs set in the read person knowledge data. When the number of appearances corresponding to the selected keyword ID exceeds a predetermined threshold value m, the person knowledge extracting unit 33 sets “1” to information indicating that the knowledge is associated with the keyword ID, that is, the knowledge flag. Is written (step S235). The threshold value m may be determined by a predetermined ratio with respect to the number of processed mail data. When the number of appearances is equal to or less than the predetermined threshold value m, “0” may be written in the knowledge flag corresponding to the keyword ID, or may be left as NULL.

  The person knowledge output unit 34 determines whether or not the process of step S235 has been completed for all keyword IDs set in the person knowledge data read in step S230, and determines that there is a keyword ID that has not been processed yet (Step S240: NO), the process of Step S235 is repeated. On the other hand, if it is determined that the process of step S235 has been executed for all the keyword IDs set in the read person knowledge data (step S240: YES), the person knowledge output unit 34 stores all from the person knowledge storage unit 14. It is determined whether or not the human knowledge data has been read (step S240). If it is determined that there is person knowledge data that has not yet been read (step S240: NO), the process from step S230 is repeated, and if it is determined that all person knowledge data has been read (step S245: YES), the process ends. To do.

  FIG. 12 shows an example of the knowledge extraction process shown in FIG. 11 described above, and shows only a part of the data. When the processed mail data reading unit 32 reads the processed mail data D21 from the processed mail data storage unit 12 (step S210), the person knowledge extracting unit 33 reads the keywords “defect” and “ The keywords “cause”,... That are not registered in the important keyword list are deleted from “cause”, “SQL”, “access”,... (Step S215). This result is post-processed mail data D22 for extracting important keywords. The person knowledge extracting unit 33 generates person knowledge data D23 in which the number of emails in which keywords remaining as a result of the deletion appear are tabulated for each sender, and writes it in the person knowledge storage unit 14 (steps S220 and S225). For each of the human knowledge data D23, a keyword whose number of appearances exceeds a predetermined threshold value m is selected and “1” is written in the knowledge flag (steps S235, S240, and S245). This result is the person knowledge data D24. In the figure, the knowledge flag “1” is set in association with the keyword IDs of the keywords “JAxx”, “basic examination”, “SQL”, and “performance evaluation”.

  In the above, all the processed mail data stored in the processed mail data storage unit 12 are read, but the processed mail in which the date and time of a predetermined period such as the past year is set in the date and time information. It is also possible to read only data.

In the above, the number of emails in which each word appears at least once is counted as the number of appearances. However, in step S135 of FIG. 9, the number of occurrences in the email text of one email is a predetermined number. The number of emails in which each keyword appears in the email body more than a predetermined number may be counted by deleting the unsatisfied keywords.
In step S135 in FIG. 9, the number of occurrences of the keyword in the text may be written in the processed mail data in association with the keyword. In step S220 of FIG. 11, the number of appearances obtained by accumulating the number of appearances associated with the keyword is set in the person knowledge data.

Further, the important keyword list may be extracted by the important keyword extraction process shown in FIG. 9 described above and registered in the knowledge map storage unit 13. For example, it may be registered in the knowledge map storage unit 13.
When the user registers the important keyword list in the knowledge map storage unit 13 by himself / herself, the process of FIG. 11 is performed after generating the processed mail data without performing the processes of steps S140, S150 and S155 of FIG.

  Alternatively, when the user himself / herself registers the important keyword list in the knowledge map storage unit 13, the process of FIG. 11 may be performed as follows without performing the process of FIG. That is, in step S210 of FIG. 11, the mail reading unit 21 of the important keyword generation unit 20 reads one piece of mail data out of the mail data not yet read from the mail data storage unit 11, and describes this mail data. The mail text is read from the file indicated by the file name of the text data that has been read, and the mail data and the mail text are output to the person knowledge extraction unit 33. In step S215, the person knowledge extracting unit 33 of the person knowledge processing unit 30 deletes the quoted part from another mail from the mail body, and adds the deleted mail body to the important keyword list read in step S205. Identify the word being described. In step S225, it is determined whether or not all mail data has been read from the mail data storage unit 11. If it is determined that there is mail data that has not been read yet, the processing from step S210 is repeated, and all the mail data is stored. If it is determined that the data has been read, the process of step S230 is performed.

Next, the knowledge map display process will be described.
Before the knowledge map display process is performed, the user generates knowledge map data and registers it in the knowledge map storage unit 13.
Specifically, the user accesses the knowledge map display processing unit 40 by the user terminal 100 and transmits a knowledge map creation request. The knowledge map creation unit 41 of the knowledge map display processing unit 40 reads out keywords from the important keyword list currently stored in the knowledge map storage unit 13 and displays them on the user terminal 100. This important keyword list may be extracted in the important keyword processing flow shown in FIG. 9 described above, or may be previously registered by the user in the knowledge map storage unit 13 by the user terminal 100 or the like. Good.

  The user selects a keyword indicating knowledge necessary for the project from the keywords displayed on the display of the user terminal 100, and inputs a correlation between the selected keywords and a knowledge map name using a keyboard, a mouse, or the like. The information is input by means and transmitted to the knowledge map display processing unit 40.

  When the knowledge map creation unit 41 of the knowledge map display processing unit 40 receives a keyword indicating knowledge, a correlation between the keywords, and a knowledge map name from the user terminal 100, the knowledge map display unit 40 corresponds to the received keyword and each of the keywords. A knowledge list is created that includes the keyword ID and the keyword ID of the keyword designated as the knowledge that is higher than the keyword. The knowledge map creation unit 41 writes the knowledge map ID in which the received knowledge map name is set and the knowledge map data in which the created knowledge list is set in the knowledge map storage unit 13.

  It is also possible to generate a knowledge map first and generate an important keyword list from the generated knowledge map. In this case, the keyword itself is received from the user terminal 100 instead of the keyword ID. The knowledge map creation unit 41 of the knowledge map display processing unit 40 assigns a keyword ID to each received keyword to create an important keyword list, and also creates knowledge map data and stores it in the knowledge map storage unit 13 as described above. Write.

FIG. 13 is a diagram illustrating a processing flow of personal knowledge display processing by the knowledge map display processing unit 40. In the personal knowledge display process, the knowledge possessed by the designated person is displayed on the knowledge map.
First, the user accesses the knowledge map display processing unit 40 from the user terminal 100, and inputs the knowledge map ID of the knowledge map to be used and the knowledge map display instruction by the input means. The knowledge map creation unit 41 of the knowledge map display processing unit 40 reads knowledge map data specified by the knowledge map ID received from the user terminal 100 from the knowledge map storage unit 13 (step S305).

  The knowledge map display unit 43 reads a keyword from the knowledge list in the read knowledge map data. Then, for each of the read keywords, the upper keyword is specified by the upper keyword ID associated with the keyword. The knowledge map display unit 43 generates screen data for displaying the keywords in the knowledge list so that each keyword is subordinate to the upper keyword of the keyword. For example, the upper keyword is displayed above the lower keyword, and a line is drawn between the upper keyword and the lower keyword. The knowledge map display unit 43 transmits the created screen data to the user terminal 100 (step S310). The user terminal 100 displays a knowledge map on the display based on the received screen data.

  Subsequently, the user inputs a person selection request transmission instruction to the user terminal 100. The person selection input unit 44 acquires person IDs from all person knowledge information stored in the person knowledge storage unit 14, and transmits a list of acquired person IDs to the user terminal 100. The user terminal 100 displays a list of received person IDs on the display. Note that the person selection input unit 44 may read personal information such as a name and affiliation corresponding to the acquired person ID from the person information data in the person information storage unit 15 and transmit the personal information to the user terminal 100 for display. The user selects a person whose knowledge is to be displayed from the person ID, name, etc. displayed on the user terminal 100 using the input means, and inputs a knowledge display instruction. The person selection input unit 44 receives the person ID of the selected person (step S315).

  The person knowledge reading unit 45 reads the person knowledge data specified by the person ID received by the person selection input unit 44 in step S315 from the person knowledge storage unit 14 (step S320). The knowledge map display unit 43 extracts the keyword ID included in the knowledge list in the knowledge map data read in step S305 from the keyword ID set in association with the knowledge flag “1” in the read person knowledge data. . The knowledge map display unit 43 generates knowledge map screen data to which information indicating that the keyword corresponding to the extracted keyword ID is the knowledge of the person specified by the received person ID is added. The knowledge map display unit 43 transmits the created screen data to the user terminal 100 (step S325). The user terminal 100 displays a knowledge map based on the received screen data on the display.

  FIG. 14 is a diagram showing a display image of the knowledge map displayed as a result of the processing of FIG. In the figure, a “commercial development” knowledge map is displayed. In the knowledge map, each keyword is displayed in an oval box (hereinafter referred to as “node”). For example, the nodes “Network”, “Server”, and “Development” are displayed under the node “Commercial Development”, and a line is drawn between these nodes, so the knowledge “Commercial Development” It is possible to visually grasp that the knowledge is higher in the knowledge “network”, “server”, and “development”.

  In the same figure, a list of persons “A employee”, “B employee”, “C employee”,... Is displayed on the left side of the knowledge map. When the user selects “C employee” whose knowledge is to be displayed with the mouse, the node of the keyword corresponding to the knowledge “SQL”, “JAxx”, “Windxxx” possessed by “C employee” is changed from another node. Are displayed in different colors. Note that the knowledge may be indicated by changing the text display color or character size of the keyword corresponding to the knowledge possessed by the selected person, or by changing the line surrounding the node. As a result, it is possible to visually grasp the knowledge possessed by the person, and it is possible to grasp the situation such as what field of knowledge is possessed.

FIG. 15 is a diagram illustrating a processing flow of team knowledge display processing by the knowledge map display processing unit 40. In the team knowledge display process, the members of the project team having each knowledge are displayed on the knowledge map.
First, the knowledge map display processing unit 40 performs the same processing as steps S305 to S310 in FIG. 13, and causes the user terminal 100 to display the knowledge map of the project team selected by the user (steps S405 and S410). Subsequently, the knowledge map display unit 43 causes the user terminal 100 to display a list of personal information such as a person ID or name or affiliation on the display by the same process as step S315 of FIG. The user is considering hiring the project team from the person IDs and names displayed on the user terminal 100, or selects the hired person using the input means and inputs a knowledge display instruction. Here, it is assumed that a plurality of persons are selected. The person selection input unit 44 receives the person ID of the selected person (step S415).

  The person knowledge reading unit 45 reads the person knowledge data specified by each person ID received by the person selection input unit 44 in step S415 from the person knowledge storage unit 14 (step S420). When one of the keyword IDs included in the knowledge list in the knowledge map data read in step S405 is selected, the knowledge map display unit 43 associates the knowledge flag with the keyword ID in the person knowledge data read in step S420. The number of person knowledge data for which “1” is set, that is, the number of knowledge holders is counted (step S425). Further, the person ID is read from the person knowledge data in which “1” is set in the knowledge flag, and is used as the knowledge holding person ID. The process of step S425 is executed for all keyword IDs included in the knowledge list in the knowledge map data (step S430).

  The knowledge map display unit 43 generates knowledge map screen data to which information on the number of knowledge holders of each keyword counted in step S420 is added. The knowledge map display unit 43 adds the knowledge holding person ID read corresponding to each keyword ID in step S425 to the created screen data and transmits it to the user terminal 100 (step S435). The user terminal 100 displays a knowledge map based on the received screen data on the display. When the user selects a keyword on the knowledge map using the input unit of the user terminal 100, the knowledge holding person ID corresponding to the keyword ID of the keyword or the name corresponding to the knowledge holding person ID is Is displayed on the knowledge map as a person having knowledge.

  FIG. 16 is a diagram showing a display image of the knowledge map displayed as a result of the processing of FIG. In the figure, a “commercial development” knowledge map is displayed. Further, on the left side of the knowledge map, a list of persons “A employee”, “B employee”, “C employee”,... Is displayed. When the user selects “A employee”, “C employee”, “E employee”, and “F employee” from the list of persons with the mouse, a team member list including the selected employees is displayed. When the team member list is input, the number of knowledge holders of the keyword is displayed at the node corresponding to each keyword. In the figure, “commercial development” knowledge holding number “2”, “server” knowledge holding number “1”, “SQL” knowledge holding number “3”,... Are displayed. When the user clicks on the node of the keyword “SQL” that the user wants to focus on, the name of the person who has the keyword as knowledge is highlighted on the team member list with the background color changed. Note that the person having the knowledge may be displayed by changing the display color or the character size of the name of the person having the knowledge, and the knowledge holding person ID corresponding to the keyword or the like may be displayed in each keyword node. You may make it display the name corresponding to the said knowledge possession person ID.

Next, team formation processing will be described.
FIG. 17 is a diagram illustrating a process flow of the team organization process.
First, a knowledge map of a project that forms a team is displayed on the user terminal 100 by the same processing as steps S305 to S310 in FIG. The user selects the knowledge required for each post group from the knowledge map using the input means of the user terminal 100, and inputs a transmission instruction. A position group is a group of positions that require the same knowledge when each role in the project is divided by position. A keyword indicating knowledge may be selected from the important keyword list. The team organization condition input unit 51 of the team organization result display processing unit 50 receives the keyword ID of the keyword selected as the knowledge required for each post group from the user terminal 100 (step S505).

  Next, the user inputs the requested team organization of the project and a transmission instruction using the input means of the user terminal 100. The request team organization is information on positions included in each position group, for example, information on leaders, subleaders included in management position groups, engineers included in managed positions, the number of each position, and consumption operation points. In addition, a person ID of a person who is a candidate for a project member or a person ID of a person to be excluded from the member candidates may be included. The team organization condition input unit 51 receives information on the requested team organization from the user terminal 100 (step S510).

  Subsequently, the user uses the input means of the user terminal 100 to obtain a team automatic organization algorithm to be applied to each post group, a processing order between post groups, a processing order of post within the same post group, and a transmission instruction. input. There are two team automatic organization algorithms: an algorithm that emphasizes individual coverage and an algorithm that focuses on collective coverage. The individual comprehensiveness-oriented algorithm is an algorithm for selecting a member having the most knowledge among the knowledge required for the position group when selecting a plurality of members of the same position group. On the other hand, when selecting multiple members of the same post group, the collectiveness-focused algorithm is the most common except for the knowledge that the person already selected as a member has in the knowledge required for the post group. This is an algorithm for selecting persons having knowledge in order. The team organization condition input unit 51 receives, from the user terminal 100, the team automatic organization algorithm to be applied to each post group, and information on the processing order between post groups and between post units (step S515).

  The team formation condition input unit 51 outputs the information received from the user terminal 100 in steps S505 to S515 to the team formation condition reception unit 61 of the team formation processing unit 60. The candidate knowledge reading unit 62 of the team organization processing unit 60 sets all the person knowledge information and the person ID of the person who becomes a member candidate if no person ID is set in the received requested team organization. For example, if the person ID of the person to be excluded from the candidates is set as the person knowledge information specified by the person ID, all the person knowledge information excluding the person knowledge information in which the person ID is set, Read from the person knowledge storage unit 14 (step S520).

  The team organizing unit 63 selects a position group having the highest priority among position groups for which members have not yet been selected based on the processing order between position groups (step S525). The team organization unit 63 determines the team automatic organization algorithm designated for the selected post group (step S530). If the designated team automatic organization algorithm is an algorithm for emphasizing personal coverage, the processing shown in FIG. 18 described later is performed to obtain a person ID of a person suitable for each position in the selected position group (step S535). On the other hand, if the designated team automatic organization algorithm is an algorithm that emphasizes collective coverage, the processing shown in FIG. 19 described later is performed to obtain a person ID of a person suitable for each position in the selected position group (step S540). .

The team organization unit 63 determines whether or not a person has been selected for all position groups (step S545). If there is still a post group for which no member has been selected (step S545: NO), the processing from step S525 is executed.
On the other hand, when the members are selected for all the job title groups (step S545: YES), the team organization result output unit 64 reads from the person ID selected for each job title or the person information data specified by the person ID. The personal information such as the name is output to the team organization result display processing unit 50. The team organization result display unit 52 of the team organization result display processing unit 50 displays each position and the person ID or personal information of the person selected for each position received in step S550 and approves the team formation result. The screen data for displaying the button for performing or reorganizing is generated and transmitted to the user terminal 100 (step S550). The user terminal 100 displays the received screen data on the display. The user confirms the person selected for each post by this display, and selects the approval button when approving, or selects the reorganization button when not approving, using the input means.

When receiving information from the user terminal 100 that the approval button has been selected (step S555: YES), the team organization condition input unit 51 of the team organization result display processing unit 50 notifies the team organization processing unit 60 of the approval. When the operating point update unit 65 identifies the person information data in the person information storage unit 15 based on the person ID of the person selected for each position, the person is a member of the operating point set in the identified person information data. It is updated to the point obtained by subtracting the consumption operation point of the post that became (step S560).
Note that the team organization condition input unit 51 receives the information indicating that the reorganization button has been selected from the user terminal 100 (step S555: NO), and performs the processing from step S505 again.

  Note that the team knowledge display process of FIG. 15 may be performed using the person ID of the person selected for each position as the input in step S415.

FIG. 18 is a diagram showing a processing flow of person selection processing by the individual comprehensiveness importance algorithm executed in step S535 of FIG.
First, the team organizing unit 63 selects a position having the highest priority among the positions belonging to the currently selected position group for which no member has been determined (step S605).

  The team organizing unit 63 further removes the person knowledge data already set as one of the positions from the person knowledge data read in step S520 in FIG. The person knowledge data in which the same person ID is set as the person information data in which the operation points less than the consumption operation points of the current job title are set is excluded (step S610).

  For each person knowledge data remaining as a result of step S610, the team organizing unit 63 sets the number in which “1” is set in the knowledge flag in association with the keyword ID indicating the knowledge required for the currently selected position. The person knowledge data with the highest count is selected (step S615).

  When the number of the person knowledge data selected in step S615 is one (step S620: YES), the team organization unit 63 sets the person ID set in the selected person knowledge data and the currently selected job title. Are stored in the storage means included in the team organization unit 63 (step S640).

  On the other hand, when there are a plurality of pieces of person knowledge data selected in step S615 (step S620: NO), the person data reading unit 66 has person information set with the same person ID as each of the selected person knowledge data. The data is read from the person information storage unit 15, and the person information data having the largest operating point is selected from the read person information data (step S625).

  When the number of the person information data selected in step S625 is one (step S630: YES), the team organizing unit 63 sets the person ID set in the selected person information data and the currently selected job title. Are stored in the storage means included in the team organizing unit 63 (step S640).

  On the other hand, when there are a plurality of pieces of personal information data selected in step S625 (step S630: NO), the team organization unit 63 randomly selects one of the selected pieces of personal information data (step S635). ), The person ID set in the selected person information data and the currently selected job title are associated with each other and stored in the storage means provided in the team organizing unit 63 (step S640).

  The team organizing unit 63 excludes the person knowledge data in which the person ID stored in association with the position in step S640 is set from the selection process target of each position (step S645). The team organizing unit 63 has positions that have not yet been selected among positions belonging to the position group that is currently being processed, that is, positions that have not reached the number indicated in the requested team formation. If there is any (step S650: NO), the processing from step S605 is performed. On the other hand, if the selection of all positions has been completed (step S650: YES), the process is terminated, and the processes after step S545 in FIG. 17 are performed. The number of members selected as members for each position corresponds to the number of person IDs stored in association with the position.

FIG. 19 is a diagram showing a processing flow of person selection processing by the collective comprehensiveness importance algorithm executed in step S540 of FIG.
In the figure, the processing from step S705 to S745 is the same as the processing from step S605 to S645 in FIG. In step S740, the team organizing unit 63 reads out the keyword ID in which “1” is set in the knowledge flag from the person knowledge data specified by the person ID written in association with the job title. The ID is excluded from the keyword ID of the knowledge required for the title (step S750). The team organizing unit 63 has positions that have not yet been selected among positions belonging to the position group that is currently being processed, that is, positions that have not reached the number indicated in the requested team formation. If there is (step S755: NO), the processing from step S705 is performed. On the other hand, when the selection of all positions has been completed (step S755: YES), the process is terminated, and the processes after step S545 in FIG. 17 are performed.

  In addition, instead of randomly selecting the person information data in step S635 or S735 in FIG. 18, the team organization condition input unit 51 uses the personal ID or name read from each of the selected plurality of person information data. The information is transmitted to the user terminal 100 and displayed together with the currently selected job title, and the person ID of the person selected based on the displayed person ID or name is received to determine the member of the job title. May be.

  Further, instead of randomly selecting the person information data in step S635 in FIG. 18, the currently selected post group and the person knowledge data in which the same person ID as the person information data selected in step S625 is selected. May select the one having the largest number of knowledge flags “1” set in association with the keyword IDs of knowledge required for different job groups, and read the person ID from the selected knowledge data.

  Further, instead of randomly selecting the person information data in step S735 in FIG. 19, the knowledge already deleted in step S750 from the person knowledge data in which the same person ID as the person information data selected in step S725 is selected. Including the keyword ID of, the one having the largest number of knowledge flags “1” set in association with the keyword ID of the knowledge required for the currently selected post group is selected, and from this selected knowledge data The person ID may be read out.

FIG. 20 is a diagram illustrating an operation example of team organization.
As shown in the figure, it is assumed that person knowledge data of each person is generated and registered in the person knowledge storage unit 14 before team formation. Here, the knowledge flag “1” is set in the person knowledge data of Mr. A corresponding to the keyword IDs of “basic examination”, “JAxx”, and “SQL”, and the person knowledge data of Mr. The knowledge flag “1” is set corresponding to the keyword IDs of “C ++” and “SQL”, and the keywords of “basic examination”, “performance evaluation”, “C ++”, and “SQL” are set in Mr. C's person knowledge data. The knowledge flag “1” is set corresponding to the ID, the knowledge flag “1” is set corresponding to the keyword ID “JAxx” in the person knowledge data of Mr. D, and the person knowledge data of Mr. E is included in the person knowledge data of E. Assume that the knowledge flag “1” is set corresponding to the keyword ID “SQL”.

  The user inputs team formation conditions to the user terminal 100 and transmits them to the team formation result display processing unit 50. First, the user receives knowledge keyword IDs required for two position groups of a management position and a managed position group from the knowledge map of a project to be formed as a team (step S505). In the figure, it is assumed that “basic examination” and “performance evaluation” are selected for the management position, and “C ++”, “JAxx”, and “SQL” are selected for the management position.

  Subsequently, the user inputs the requested team organization to the user terminal 100. Here, it is assumed that information indicating that the management position group is composed of one position “leader” and one position “sub-leader” and the management position group is composed of two “engineers” is input. To do. In addition, the user must apply the algorithm for emphasizing individual coverage to management groups, apply the algorithm for emphasis on collective coverage to managerial positions, select managerial groups after management groups, and follow leaders. Enter the sub leader selection.

  The team organization processing unit 60 and the team organization unit 63 first select a manager with a high priority, but select a leader with a higher priority among the positions belonging to the manager. The team organizing unit 63 corresponds to the keyword IDs of the knowledge “basic examination” and “performance evaluation” required for the managerial position, and C is the “2” having the largest number of knowledge flags “1”. Select person's knowledge data. Therefore, the post “leader” and the person ID of “Mr. C” are stored in association with each other. Since the member selected as the leader has reached one person designated in the requested team organization, the selection of leaders is completed.

  Subsequently, the team organization unit 63 selects a sub-leader having the next highest priority among the positions belonging to the management position. The team organizing unit 63 corresponds to the keyword IDs of the knowledge “basic examination” and “performance evaluation” required for the management staff, except for the knowledge data of Mr. C who has already been selected as the leader, The person knowledge data of Mr. A who is “1” with the largest number set to “” is selected. Therefore, the post “sub leader” and the person ID of “Mr. A” are stored in association with each other. Since the member selected as the sub-leader has reached one person designated in the requested team organization, the selection of managerial positions is completed.

  When the managerial selection is completed, the team organizing unit 63 selects a managerial job that is the next priority of the management job. The team organizing unit 63 corresponds to the keyword IDs of knowledge “C ++”, “JAxx”, and “SQL” required for engineers belonging to the managerial position, and Mr. C who has already been selected as the leader and the sub leader The person knowledge data of Mr. B who is “2” having the largest number of knowledge flags “1” is selected, except for the person knowledge data of Mr. A selected as. Therefore, the job title “engineer” and the person ID of “Mr. B” are stored in association with each other.

  The team organizing unit 63 has a knowledge flag in the person knowledge data of Mr. B selected as a member among the keyword IDs of knowledge “C ++”, “JAxx”, and “SQL” required by the engineer. The keyword IDs “C ++” and “SQL” for which “1” is set are excluded. Then, in response to the keyword ID of “JAxx” remaining as knowledge required for the engineer, Mr. C who has already been selected as a reader, Mr. A who has been selected as a sub-leader, and B who has been selected as an engineer The person knowledge data of Mr. D who is “1” having the largest number of knowledge flags “1” is selected excluding Mr. person knowledge data. Therefore, the title “engineer” and the person ID of “Mr. D” are stored in association with each other. Since the members selected as engineers have reached the two specified in the requested team organization, the selection of the managerial job is completed.

As described above, the human resource management support apparatus 1 selects a person so that the knowledge of the person engaged in each position of the new project team satisfies the knowledge necessary for each position in the project team. Organize.
However, when a person is selected in this way, a person who has a lot of knowledge may be selected as a member of many projects. Therefore, the members of the project are selected in consideration of the operation of each position in the project team and the operation of each person in the entire project currently being executed by the entire company.

FIG. 21 is a diagram showing an outline of the operation of selecting a person considering operation.
The user sets the current operating point in advance in each person information data. Then, when team formation is performed, the user inputs a consumption operation point indicating the expected operation size for each position to the user terminal 100.

Then, when the members of each job title selected as shown in FIG. 20, the consumption job points corresponding to the job title of the person are selected from the job points set in the person information data of the person selected as the member. Subtract. For example, when the consumption operation point of the reader is “2” and “4” is set as the operation point of the personal information data of Mr. C selected as the reader, the operation point is updated to “2”. . Similarly, the operating point “5” set in the personal information data of Mr. A selected as the sub leader is updated to “3” obtained by subtracting the consumption operating point “2” of the sub leader. Further, the operating point “5” set in the personal information data of Mr. B as the engineer is updated to “1” obtained by subtracting the operating operation point “4” of the engineer. The same applies to Mr. D.
Then, when “3” is designated as the consumption operation point of the leader of the other project when the other project is selected next time, Mr. B and Mr. C whose current operation point is “3” or less. , D is excluded and the reader is selected.

  If the main work of the leader of the project that selects people is limited to participation in the examination of important matters in the project, and the operation of the project is reduced to allow participation in other projects, relatively small consumption If an operation point is set, or if the operation time in one project is long like a programmer, it is difficult to concurrently use many projects, so a relatively large consumption operation point is set. As described above, by making it possible to freely set the required operation amount of each position in the project and the condition of the remaining operation amount of each person, a team formation considering the overall operation balance is realized.

  In addition, when there are few remaining operations and there are no candidates, the team formation may be executed while allowing negative operation points. That is, the person knowledge data having the largest operating point is selected, or the processing of step S610 of FIG. 18 and S710 of FIG. 19 is not performed.

As described above, in the present embodiment, knowledge necessary for a new project is freely selected, and information on whether or not each person has the knowledge is based on emails sent and received in the past. Can be collected flexibly. Therefore, even if the knowledge required for each project is different, it is possible to collect information on whether or not each person has the knowledge using the same past mail. It is also possible to extract keywords that are considered to represent knowledge from the stored mail.
In addition, the knowledge map display is converted into a GUI (Graphic User Interface), and based on the extracted knowledge information of each person, the knowledge that the specific person has and the person who has the specific knowledge are displayed on the knowledge map The distribution of knowledge of each person or team can be visualized and displayed.

  Furthermore, according to the present embodiment, it is possible to automatically select members who are engaged in organizational activities based on the collected knowledge information of each person. Previously, it was difficult to form a team based on the amount of operation according to the role in the project, but in this embodiment, the operation of each role in organizational activities and the overall operation of each person Organizational organization considering balance can be performed. In addition, it is possible to select a person by selecting whether it is necessary to widely acquire knowledge necessary for the role of each individual or whether it is possible to cover the necessary knowledge for all persons engaged in the same role.

  The human resource management support apparatus 1 and the user terminal 100 have a computer system inside. The important keyword generation unit 20, the person knowledge processing unit 30, the knowledge map display processing unit 40, the team organization result display processing unit 50, the team organization processing unit 60, and the user terminal 100 of the human resource management support apparatus 1 described above. The operation process is stored in a computer-readable recording medium in the form of a program, and the above-described processing is performed by the computer system reading and executing the program. Here, the computer system includes a CPU, various memories, an OS, and hardware such as peripheral devices.

Further, the “computer system” includes a homepage providing environment (or display environment) if a WWW system is used.
The “computer-readable recording medium” refers to a storage device such as a flexible medium, a magneto-optical disk, a portable medium such as a ROM and a CD-ROM, and a hard disk incorporated in a computer system. Furthermore, the “computer-readable recording medium” dynamically holds a program for a short time like a communication line when transmitting a program via a network such as the Internet or a communication line such as a telephone line. In this case, a volatile memory in a computer system serving as a server or a client in that case, and a program that holds a program for a certain period of time are also included. The program may be a program for realizing a part of the functions described above, and may be a program capable of realizing the functions described above in combination with a program already recorded in a computer system.

DESCRIPTION OF SYMBOLS 1 ... Human resource management support apparatus 11 ... Mail data storage part 12 ... Processing mail data storage part 13 ... Knowledge map storage part 14 ... Person knowledge storage part 15 ... Person information storage part 20 ... Important keyword generation part 21 ... Mail reading part 22 ... Important Keyword Extraction Unit 23 ... Processing Mail Data Output Unit 24 ... Important Keyword List Output Unit 30 ... Personal Knowledge Processing Unit 31 ... Important Keyword List Reading Unit 32 ... Processing Mail Data Reading Unit 33 ... Personal Knowledge Extraction Unit 34 ... Personal Knowledge Output Unit DESCRIPTION OF SYMBOLS 40 ... Knowledge map display processing part 41 ... Knowledge map creation part 42 ... Knowledge map reading part 43 ... Knowledge map display part 44 ... Person selection input part 45 ... Person knowledge reading part 50 ... Team organization result display processing part 51 ... Team organization condition Input unit 52 ... Team organization result display unit 60 ... team organization processing unit 61 ... team organization condition receiving unit 62 ... candidate knowledge reading unit 63 ... team organization unit 64 ... team organization result output unit 65 ... operating point update unit 66 ... person data reading unit 100 ... user terminal

Claims (8)

A mail data storage unit for storing mail data;
A knowledge map storage unit for storing keywords representing knowledge;
A person knowledge storage unit that stores person knowledge data associated with person identification information and knowledge;
A mail reading unit for reading the mail data from the mail data storage unit;
An important keyword list reading unit for reading the keywords from the knowledge map storage unit;
The number of the mail data in which the keyword read by the important keyword list reading unit among the mail data read by the mail reading unit appears in the mail body, for each person set as a sender in the mail data A person knowledge extraction unit that extracts a keyword in which the number of pieces of the mail data calculated for the mail data including each person as a sender is equal to or more than a threshold, as knowledge held by each person;
A person knowledge output unit that writes person knowledge data that associates the person identification information that identifies each person with the knowledge extracted by the person knowledge extraction unit for each person into the person knowledge storage unit;
A human resource management support device characterized by comprising: A word having a predetermined part-of-speech is selected from the mail text indicated by the mail data read by the mail reading unit, and a keyword is extracted from the selected word excluding a predetermined word, and the extracted keyword is included in the mail text. An important keyword extraction unit that counts the number of occurrences and selects keywords whose total number of occurrences is equal to or greater than a threshold;
An important keyword list output unit for writing the keywords selected by the important keyword extraction unit into the knowledge map storage unit;
The human resource management support apparatus according to claim 1, further comprising: The knowledge map storage unit further stores knowledge map data including keywords representing knowledge necessary for each project,
A knowledge map reading unit for reading the knowledge map data from the knowledge map storage unit;
A person knowledge reading unit that reads the person knowledge data from the person knowledge storage unit based on designated person specifying information;
The keyword included in the knowledge map data read by the knowledge map reading unit is displayed, and among the keywords included in the knowledge map data, included in the person knowledge data read by the person knowledge reading unit. A knowledge map display unit that identifies a keyword representing knowledge and displays information indicating that the identified keyword is included as knowledge;
The human resource management support apparatus according to claim 1 or 2, characterized in that The knowledge map storage unit further stores knowledge map data including keywords representing knowledge necessary for each project,
A knowledge map reading unit for reading the knowledge map data from the knowledge map storage unit;
A person knowledge reading unit for reading the person knowledge data from the person knowledge storage unit;
Displaying the keyword included in the knowledge map data read by the knowledge map reading unit, and selecting the person knowledge data including the knowledge corresponding to the keyword from the person knowledge data read by the person knowledge reading unit, A knowledge map display unit for displaying information on the person specified by the person specifying information in the selected person knowledge data corresponding to the keyword;
The human resource management support apparatus according to claim 1 or 2, characterized in that A candidate knowledge reading unit for reading the person knowledge data from the person knowledge storage unit;
From the person knowledge data read by the candidate knowledge reading section, a team organization section that identifies person knowledge data including knowledge required for a position,
A team organization result display unit for displaying information on a person specified by the person specifying information in the person knowledge data specified by the team organization unit;
The human resource management support device according to any one of claims 1 to 4, wherein the human resource management support device is provided. A personal information storage unit for storing operating points associated with each person specifying information;
The team organizing unit is required for the job title and the person specifying information associated with the operation points more than the consumption job points required for the job title among the person knowledge data read by the candidate knowledge reading unit. Identify the person knowledge data that contains the knowledge,
The human resource management support apparatus according to claim 5. Human resource management support apparatus comprising: a mail data storage unit for storing mail data; a knowledge map storage unit for storing keywords representing knowledge; and a person knowledge storage unit for storing person knowledge data associated with person identification information and knowledge Human resource management support method used for
A mail reading process in which the mail reading unit reads the mail data from the mail data storage unit;
An important keyword list reading unit reads the keyword from the knowledge map storage unit;
The person knowledge extraction unit sets the number of the mail data in which the keyword read in the important keyword list reading process among the mail data read in the mail reading process appears in the mail body as the sender in the mail data. A person knowledge extracting process of extracting, as a knowledge possessed by each person, a keyword in which the number of the mail data calculated for each person who has been collected and mail data for each person as a sender is equal to or greater than a threshold;
A person knowledge output unit writes person knowledge data in which the person identification information for identifying each person is associated with the knowledge extracted in the person knowledge extraction process for each person in the person knowledge storage unit. Process,
A human resource management support method characterized by comprising: A computer used as a human resources management support device
A mail data storage unit for storing mail data;
A knowledge map storage unit for storing keywords representing knowledge,
A person knowledge storage unit for storing person identification data and person knowledge data associated with the knowledge;
A mail reading unit for reading the mail data from the mail data storage unit;
An important keyword list reading unit for reading the keywords from the knowledge map storage unit;
The number of the mail data in which the keyword read by the important keyword list reading unit among the mail data read by the mail reading unit appears in the mail body, for each person set as a sender in the mail data A person knowledge extracting unit that extracts, as knowledge possessed by each person, a keyword in which the number of pieces of the mail data counted for the mail data having each person as a sender is equal to or greater than a threshold;
A person knowledge output unit that writes person knowledge data in which the person identification information for identifying each person is associated with the knowledge extracted by the person knowledge extraction unit for each person in the person knowledge storage unit;
A computer program that functions as a computer program.

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