JP2021192237A - Related score calculation system, method and program - Google Patents

Abstract

To provide a related score calculation system capable of clarifying the strength of the relationship between a person in an organization and a word.SOLUTION: A collection unit 86 collects schedule information describing a person, an event name, and a time zone in which the person has been involved in an event having the event name. A word extraction unit 87 extracts a word from the event names described in each piece of schedule information. A related score calculation unit 88 calculates a related score indicating a strength of a relationship between the person and the word based on each piece of schedule information.SELECTED DRAWING: Figure 20

Description

The present invention relates to a related score calculation system, a related score calculation method, and a related score calculation program for quantifying the strength of a relationship between a person and a word.

Patent Document 1 determines an application program used by a user by an operation log including information necessary for determining how much the application program installed in a personal computer or a personal digital assistant is used. The information providing device to be used is described. Further, Patent Document 1 also describes that the information providing device determines how much the user is using the application program and determines the knowledge level of the user for the application program.

Patent Document 2 describes a human resources search system that registers, deletes, updates, etc., information about human resources in a profile database, and searches for human resources that match the search keyword by referring to the profile information. Further, Patent Document 2 describes that information on the specialized field and business of human resources can be obtained by extracting keywords of a document in which a person is the author and obtaining high-ranking keywords.

Japanese Unexamined Patent Publication No. 2013-37584 Japanese Unexamined Patent Publication No. 2005-327028

It is preferable to be able to find people who are familiar with a specific field or technology or who have participated in a certain project within an organization such as a company. It is also preferable to be able to easily know the fields and technologies that a person is familiar with and the projects that the person has participated in. However, in a large organization such as a large company, "who is familiar with which field and which technology", "who has participated in which project", etc. become knowledge that can only be understood by the organization for many years. It ends up. In particular, the tendency becomes stronger for information such as "who was familiar with which field and which technology in the past" and "who participated in which project in the past". Therefore, especially for new employees and dispatched employees, it becomes difficult to know who to ask what they want to hear. As a result, for example, the efficiency of product development may decrease.

Therefore, the inventors of the present invention considered that it is preferable to be able to clarify the strength of the relationship between the person in the organization and the word. For example, if the inventors of the present invention can clarify the strength of the relationship between a person and the word "artificial intelligence", whether the person is familiar with the field or technology of "artificial intelligence" and the person I thought it would be easy to estimate if he had participated in a project on "artificial intelligence".

Therefore, an object of the present invention is to provide a related score calculation system, a related score calculation method, and a related score calculation program that can clarify the strength of the relationship between a person and a word in an organization.

Further, the related score calculation system according to the present invention describes a person, an event name, and a collection unit that collects schedule information describing the time zone in which the person was involved in the event having the event name, and each schedule information. It is equipped with a word extraction unit that extracts words from the event names, and a related score calculation unit that calculates a related score that indicates the strength of the relationship between a person and a word based on each schedule information. However, each event that includes one word in the event name for the total event participation time of all people in the organization for each event that includes one word in the event name and the related score of one person and one word. The ratio of the total event participation time of one person for each event and the total sum of event participation time of one person for each event that includes a word in the event name are calculated for each word. It is characterized in that it is calculated based on the ratio of the total event participation time of one person for each event including the word in the event name.

Further, in the related score calculation method according to the present invention, the computer collects schedule information describing a person, an event name, and a time zone in which the person is involved in an event having the event name, and describes the schedule information in each schedule information. Words are extracted from the event name, and based on each schedule information, the relation score indicating the strength of the relation between the person and the word is calculated, and when the relation score is calculated, one person and one word The related score of is the sum of the event participation time of all people in the organization for each event that includes one word in the event name, and the event participation time of one person for each event that includes one word in the event name. For each event that includes one word in the event name for each word when the ratio of the sum of the total and the sum of the event participation time of one person for each event that includes the word in the event name is calculated for each word. It is characterized in that it is calculated based on the ratio of the total event participation time of one person.

Further, the related score calculation program according to the present invention is a collection process for collecting schedule information describing a person, an event name, and a time zone in which the person is involved in an event having the event name, and each schedule information. A word extraction process for extracting words from the event name described in, and a related score calculation process for calculating a related score indicating the strength of the relationship between a person and a word based on each schedule information are executed and related. In the score calculation process, the related score of one person and one word is included in the event name. One word is used as the event name for the total event participation time of all people in the organization for each event. For each word, the ratio of the total event participation time of one person for each event including the event and the total event participation time of one person for each event containing a word in the event name are calculated for each word. , It is characterized in that it is calculated based on the ratio of the total event participation time of one person for each event including one word in the event name.

According to the present invention, it is possible to clarify the strength of the relationship between a person and a word in an organization.

It is a block diagram which shows the structural example of the related score calculation system of 1st Embodiment of this invention. It is a schematic diagram which shows the example of the operation log. It is a schematic diagram which shows the example of the relation score calculated for each combination of a user and a word. It is a flowchart which shows the example of the processing progress of the relation score calculation system of 1st Embodiment. It is a block diagram which shows the modification of 1st Embodiment. It is a schematic diagram which shows the example of the set of the user name, the word, and the relation score stored in the storage part. It is a block diagram which shows the structural example of the related score calculation system of the 2nd Embodiment of this invention. It is a schematic diagram which shows the example of the relation score calculation result (the set of the set of the user name, the word, and the relation score). It is a schematic diagram which shows the example of the 1st table. It is a schematic diagram which shows the example of the 2nd table. It is a flowchart which shows the example of the processing progress of 2nd Embodiment. It is a flowchart which shows the example of the process of calculating the search score of a selected user and a keyword word. It is a flowchart which shows the example of the process of calculating the search score of the score calculation target word and the keyword user name. It is a block diagram which shows the structural example of the related score calculation system of the 3rd Embodiment of this invention. It is a schematic diagram which shows the example of the screen which shows the clustering result. It is a block diagram which shows the structural example of the related score calculation system of 4th Embodiment. It is a schematic diagram which shows the example of the schedule information. It is a schematic block diagram which shows the structural example of the computer which concerns on each embodiment of this invention. It is a block diagram which shows the outline of this invention. It is a block diagram which shows another example of the outline of this invention.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.

Embodiment 1.
FIG. 1 is a block diagram showing a configuration example of a related score calculation system according to the first embodiment of the present invention. However, FIG. 1 also illustrates a device connected to the related score calculation system of the present invention via a communication network.

A personal computer (hereinafter referred to as a PC) 91 is connected to the related score calculation system 1 of the present invention via a communication network 10.

The PC 91 is used, for example, by a person who belongs to an organization such as a company or a company. It is assumed that each PC 91 is provided in one organization. Hereinafter, the case where the organization is a company will be described as an example. However, the organization to which the person who uses the PC91 belongs does not have to be a company or a company. Further, the organization may be an organization formed by a plurality of companies or the like, or may be a department of one company or a company or the like.

Each PC 91 creates and stores one operation log related to the file by the user who operates the file. As will be described later, the operation log stored in each PC 91 is collected by the related score calculation system 1 (more specifically, the collecting unit 2 of the related score calculation system 1).

FIG. 2 is a schematic diagram showing an example of an operation log created by the PC 91. The operation log associates, for example, the file name, the user name of the user who operated the file, the content of the operation, and the date and time when the operation was performed (see FIG. 2). In FIG. 2, the operation log numbers are also shown for convenience. Note that FIG. 2 is an example of an operation log, and the operation log is not limited to the example shown in FIG.

The PC 91 describes a file name including a path name as a file name in the operation log.

Further, the PC 91 may determine the user name described in the operation log from, for example, an ID (Identification) used when the user logs in to the PC 91. However, the method by which the PC 91 determines the user name is not limited to this method.

In each embodiment, a user name is used as a user ID (user identification information).

Examples of the operation contents described in the operation log include "file open", "key touch", "update (save)", "file close" and the like. However, the content of the operation described in the operation log is not limited to these, and may be "new creation" or the like.

For example, if the user "Yamada" opens the file "/ .../ Artificial Intelligence / ... / A Company's Machine Learning.pptx" at 13:15 on October 10, 2017, the PC91 will be shown in the figure. Create the first operation log illustrated in 2. Further, for example, when the user "Yamada" performs a key touch (key input) on the file at 13:16 on October 10, 2017, the PC 91 is the second example illustrated in FIG. Create an operation log of. Further, for example, when the user "Yamada" updates (saves) the file at 15:17 on October 10, 2017, the PC 91 creates the m-1st operation log illustrated in FIG. do. Further, for example, when the user "Yamada" closes the file at 15:45 on October 10, 2017, the PC 91 creates the m-th operation log illustrated in FIG. 2.

The operation logs from the m + 1st to the nth shown in FIG. 2 are examples of operation logs when the user "Yamada" operates another file "/.../ artificial intelligence / ... / deep learning.docx". ..

Similarly, each PC 91 adds and stores an operation log each time the user performs an operation on the file.

The related score calculation system 1 includes a collection unit 2, a word extraction unit 3, a score calculation unit 4, and a storage unit 5.

The collecting unit 2 collects the operation log stored in each PC 91 from each PC 91. As shown in FIG. 2, each operation log includes a file name including a path name and a user name of a user who used a file having the file name. Each operation log also contains information from which the association score, which is an index value indicating the strength of the association between the user and the word, can be derived. In the example shown in FIG. 2, the information described as "date and time" and "operation" corresponds to the information from which the related score can be derived. However, the related score is not derived from one operation log, but is derived from a plurality of operation logs.

The word extraction unit 3 performs morphological analysis on each file name (file name including the path name) described in each operation log collected by the collection unit 2 to obtain a file name including the path name. Extract the contained words. However, the word extraction unit 3 does not extract the same word in duplicate. For example, when the word extraction unit 3 has already extracted the word "artificial intelligence", the word "artificial intelligence" extracted from the second time onward is ignored.

For example, the word extraction unit 3 performs morphological analysis on a file name including the path name “/ ・ ・ / artificial intelligence / ・ ・ / machine learning of company A.pptx” to obtain “artificial intelligence”. Extract words such as "Company A" and "machine learning". In the following description, it is assumed that the company (organization) to which each user belongs is "Company A".

Further, for example, the word extraction unit 3 performs morphological analysis on a file name including the path name “/ ・ ・ / artificial intelligence / ・ ・ / deep learning.docx” to obtain “artificial intelligence” and “artificial intelligence”. Extract words such as "deep learning". However, as described above, since "artificial intelligence" has already been extracted, the word extraction unit 3 ignores the word "artificial intelligence" extracted here.

The word extraction unit 3 obtains a set of words by performing the same processing for each file name described in each operation log. These words are different from each other.

The score calculation unit 4 calculates the association score, which is an index value indicating the strength of the association between the user and the word, for each combination of each user who operated the file and each word extracted by the word extraction unit 3. The user is represented by a user name described in the operation log.

In the second embodiment of the present invention, an index value indicating the strength of the relationship between words is also used. In the present invention, the index value indicating the strength of the relationship between the user and the word is referred to as "relationship score", and the index value indicating the strength of the relationship between words is referred to as "relevance degree". Distinguish between values.

The score calculation unit 4 calculates a related score for each combination of a user (user name) and a word, and stores the set of the user name, the word, and the related score in the storage unit 5.

The storage unit 5 is a storage device that stores a set of a user (user name), a word, and a related score.

There are multiple methods for calculating the related score. Hereinafter, three types of methods will be described as methods for calculating the related score. In any of the three types of methods shown below, the score calculation unit 4 calculates the related score for each combination of the user and the word, and stores it in the storage unit 5.

The first calculation method is the user U for each file containing the word W in the file name as the association score between one user (hereinafter referred to as user U) and one word (hereinafter referred to as word W). It is a method of calculating the total operation time of. It can be said that the longer the operation time, the stronger the relationship between the user U and the word W, and the shorter the operation time, the weaker the relationship between the user U and the word W. Therefore, the operation time can be used as a related score.

Here, first, a case where the actual reference time is treated as the operation time will be described as an example.

The file name is a file name including a path name. Therefore, even if the word W is included in the path name, the word W is treated as if it is included in the file name. This point is the same in the second calculation method and the third calculation method described later.

The actual reference time is the time during which the window representing the contents of the file is active on the PC91 (that is, the time when the window representing the contents of the file is displayed in the foreground when viewed from the user). be.

In the first calculation method of the association score, the score calculation unit 4 calculates the sum of the actual reference times of the user U for each file including the word W in the file name, and the sum is the relationship between the user U and the word W. Let it be a score.

If a key touch has been made, the window showing the contents of the file is active. Therefore, for example, the score calculation unit 4 determines from the operation log the state in which the key touch is continuously performed by the user U for one file containing the word W in the file name, and the first key touch time in that state is determined. The time from to the last key touch time is taken as the actual reference time of the user U in the file.

Further, if the file name includes the word W and there is another file operated by the user U, the score calculation unit 4 similarly calculates the actual reference time for that file.

Then, the score calculation unit 4 includes the word W in the file name, calculates the sum of the actual reference times calculated for each file operated by the user U, and sets the sum as the related score between the user U and the word W.

If the operation log clearly indicates the start time and end time of the active state, the score calculation unit 4 calculates the actual reference time based on those times specified in the operation log. You may.

Further, the score calculation unit 4 may calculate the time from the file opening to the file closing as the operation time. In this case, the score calculation unit 4 includes the word W in the file name, calculates the time from the file opening to the file closing for each file operated by the user U, and the sum of the times is the sum of the time for the user U and the word W. It can be a related score.

It is preferable to treat the actual reference time as the operation time.

The second calculation method of the relation score is a key touch when the user U operates each file containing the word W in the file name as the relation score of one user (user U) and one word (word W). This is a method of calculating the total number of times. As the number of key touches increases, the amount of operations by the user U on the file containing the word W in the file name increases. Therefore, it can be said that the greater the number of key touches, the stronger the relationship between the user U and the word W, and the smaller the number of key touches, the weaker the relationship between the user U and the word W. Therefore, the number of key touches can be used as the related score.

In the second calculation method, the score calculation unit 4 refers to the operation log and counts the number of key touches when the user U operates one file containing the word W in the file name. Find the number of key touches in.

Further, if the file name includes the word W and there is another file operated by the user U, the score calculation unit 4 similarly obtains the number of key touches for that file.

Then, the score calculation unit 4 includes the word W in the file name, calculates the total number of key touches calculated for each file operated by the user U, and sets the total as the related score between the user U and the word W. ..

The third method of calculating the association score is a method of calculating the association score of one user (user U) and one word (word W) based on the two ratios described below. Of these two ratios, one ratio is referred to as R ₁ and the other ratio is referred to as R ₂ .

R ₁ is, to the sum of the operation times of all the users in the organization for each file containing the word W in the file name, which is the ratio of the sum of the operation time of the user U for each file containing the word W in the file name. That is, R ₁ is represented by the following equation (1).

R ₂ is the operation time of the user U for each file containing the word W in the file name with respect to the total operation time of the user U for each file containing the word of interest in the file name when focusing on each word. It is the ratio of the total of. That is, R ₂ is represented by the following equation (2).

R ₁ will be described. The sum of the operation times of the user U (the numerator on the right side of the equation (1)) for each file containing the word W in the file name corresponds to the association score calculated by the first calculation method described above. That is, the score calculation unit 4 may calculate the total operation time of the user U for each file including the word W in the file name by the method described in the first calculation method described above.

The sum of the operation times of all users in the organization for each file containing the word W in the file name (the denominator on the right side of the equation (1)) will be described. The score calculation unit 4 also calculates the total operation time of the first user in the organization for each file containing the word W in the file name by the method described in the first calculation method described above. Similarly, the score calculation unit 4 also calculates the total operation time of the second user in the organization for each file containing the word in the W file name by the method described in the first calculation method described above. Similarly, the score calculation unit 4 calculates the total operation time of the user for each file including the word W in the file name for each person belonging to the organization. Further, the score calculation unit 4 calculates the sum of "the sum of the user's operation times for each file containing the word W in the file name" calculated for each person belonging to the organization. This value corresponds to the total operating time of all users in the organization for each file containing the word W in the file name.

For example, assume that the word W is "artificial intelligence" and the user U is "Yamada". Further, it is assumed that there are 300 users in the organization "Company A" to which Yamada belongs. In this case, the score calculation unit 4 obtains the total operation time of the user "Yamada" for the file containing "artificial intelligence" in the file name as the numerator on the right side of the equation (1). Further, the score calculation unit 4 calculates the total operation time of the user for the file containing "artificial intelligence" in the file name for each of 300 individual users, and further calculates the "operation" for each individual user. The sum of "sum of time" is calculated as the denominator on the right side of equation (1). Then, the score calculation unit 4 calculates R ₁ by the equation (1).

Next, R ₂ will be described. The molecule on the right side of the formula (2) is the same as the molecule on the right side of the formula (1). Therefore, the score calculation unit 4 may calculate the total operation time of the user U for each file including the word W in the file name by the method described in the first calculation method described above.

The sum of the operation times of the user U (the denominator on the right side of the equation (2)) for each file including the word of interest in the file name when focusing on each word will be described. The score calculation unit 4 pays attention to the individual words extracted by the word extraction unit 3 (in other words, selects each individual word one by one). Then, the score calculation unit 4 calculates the total operation time of the user U for each file including the word of interest (selected word) in the file name by the method described in the first calculation method described above. The score calculation unit 4 focuses on the next word (in other words, selects the next word), and sums up the operation time of the user U for each file containing the focused word (selected word) in the file name. , Calculate by the method described in the above-mentioned first calculation method. In this way, the score calculation unit 4 calculates the total operation time of the user U for each file including the word in the file name for each word. Then, the score calculation unit 4 calculates the sum of "the sum of the operation times of the user U for each file including the word in the file name" calculated for each word. This value corresponds to the denominator on the right side of equation (2). "The total operation time of the user U for each file containing the word of interest in the file name when focusing on each word (the denominator on the right side of equation (2))" is "each including the word in the file name." It can be said that it is "the sum of the sums when the sum of the operation times of the user U for the file is obtained for each word".

For example, as in the above example, it is assumed that the word W is "artificial intelligence" and the user U is "Yamada". In this case, the score calculation unit 4 obtains the total operation time of the user "Yamada" for the file containing "artificial intelligence" in the file name as the numerator on the right side of the equation (2). In addition, the score calculation unit 4 sums up the operation time of the user "Yamada" for the file containing the word in the file name for each extracted word such as "artificial intelligence", "company A", and "deep learning". calculate. Further, the sum of the "sum of operation times" calculated for each word is obtained as the denominator on the right side of the equation (2). Then, the score calculation unit 4 calculates R ₂ by the equation (2).

_{After obtaining R 1} and R ₂ , the score calculation unit 4 calculates the related score between the user U and the word W by the following equation (3).

Related score = R ₁ x log (R ₂ ) ・ ・ ・ (3)

When the association score is calculated by this third calculation method, the value of the association score is low for words related to many people belonging to the organization, and the value of the association score is low for words related to a specific person belonging to the organization. The value of the related score is high. For example, since each user belongs to company A, the word "company A" is considered to be related to each user. However, it can be said that it is self-evident that the word "Company A" is strongly related to each user. Therefore, it does not make much sense to increase the association score between the word "Company A" and each user, and it is preferable to decrease the association score. Further, when only a specific user belonging to an organization is associated with the word "artificial intelligence", it is preferable that the association score between the user and the word "artificial intelligence" is high. In the third calculation method, the related score can be calculated as such.

FIG. 3 is a schematic diagram showing an example of the association score calculated for each combination of the user and the word. The first association score shown in FIG. 3 is the association score calculated by the first calculation method. The second association score is the association score calculated by the second calculation method. The third association score is the association score calculated by the third calculation method. Although three types of related scores are shown in FIG. 3, the score calculation unit 4 may calculate any one type of related score. However, the score calculation unit 4 may calculate two or more types of related scores.

As described above, the score calculation unit 4 calculates the association score for each combination of the user and the word, and stores the pair of the user name, the word, and the association score in the storage unit 5.

The collecting unit 2 is realized by, for example, a CPU (Central Processing Unit) of a computer operating according to a related score calculation program and a communication interface of the computer. For example, the CPU may read the related score calculation program from a program recording medium such as a program storage device of a computer, and operate as the collecting unit 2 by using the communication interface according to the related score calculation program. Further, the word extraction unit 3 and the score calculation unit 4 are also realized by, for example, the CPU of the above-mentioned computer that operates according to the related score calculation program. That is, as described above, the CPU that has read the related score calculation program may operate as the word extraction unit 3 and the score calculation unit 4 according to the related score calculation program. The storage unit 5 is realized by the storage device of the above-mentioned computer. Further, the collection unit 2, the word extraction unit 3, and the score calculation unit 4 may be realized by separate hardware.

Further, the related score calculation system 1 may be configured such that two or more physically separated devices are connected by wire or wirelessly. This point is the same in other embodiments described later.

Next, the processing progress of the first embodiment will be described. FIG. 4 is a flowchart showing an example of the processing progress of the related score calculation system of the first embodiment. The details of the matters already described will be omitted.

First, the collection unit 2 collects operation logs from each PC 91 provided in the company (step S1).

Next, the word extraction unit 3 extracts words by performing morphological analysis on the file names (file names including path names) described in each operation log (step S2).

Next, the score calculation unit 4 sets the user and the word represented by the user name for each combination of the user name described in the operation log and the word extracted in step S2 based on each operation log. Calculate the association score that represents the strength of the association. Then, the score calculation unit 4 stores the set of the user name, the word, and the related score in the storage unit 5 (step S3). The score calculation unit 4 may calculate the related score by any of the above-mentioned first calculation method, second calculation method, and third calculation method.

As a result, a plurality of pairs of the user name, the word, and the related score are stored in the storage unit 5.

According to the present embodiment, the score calculation unit 4 calculates the related score for each combination of the user and the word based on each operation log. Then, in each of the above-mentioned first calculation method, second calculation method, and third calculation method, basically, the amount of operation of the user U on the file containing the word W in the file name (key touch). The larger the number of times, the operation time, etc.), the larger the value is calculated as the related score. Therefore, the strength of the relationship between the user and the word is appropriately quantified for each combination of the user and the word. Therefore, it is possible to clarify the strength of the relationship between a person and a word in an organization.

Next, a modification of the first embodiment will be described. FIG. 5 is a block diagram showing a modified example of the first embodiment. The related score calculation system 1 shown in FIG. 5 includes a keyword reception unit 6, a search unit 7, and an output unit 8 in addition to the collection unit 2, the word extraction unit 3, the score calculation unit 4, and the storage unit 5. The same elements as those shown in FIG. 1 are designated by the same reference numerals as those shown in FIG. 1, and the description thereof will be omitted.

In the example shown below, it is assumed that a plurality of pairs of the user name, the word, and the related score calculated by the above-mentioned third calculation method are stored in the storage unit 5. FIG. 6 is a schematic diagram showing an example of a plurality of sets stored in the storage unit 5.

The related score calculation system 1 illustrated in FIG. 5 accepts a word as a search keyword and searches for the user name of the user corresponding to the word. Alternatively, the related score calculation system 1 accepts the user name as a search keyword and searches for a word corresponding to the user name. Further, the related score calculation system 1 may be able to execute each of the above two types of searches.

The keyword reception unit 6 receives a search keyword from a searcher.

The search unit 7 executes a search according to the search keyword.

The output unit 8 outputs the search result.

The keyword receiving unit 6 receives a search keyword from, for example, a terminal device (not shown) used by the searcher via a communication network, and the output unit 8 transmits the search result to the terminal device. do it. Hereinafter, a case where the keyword receiving unit 6 accepts the search keyword and the output unit 8 outputs the search result in this way will be described as an example. However, the mode of accepting search keywords and the mode of outputting search results are not limited to this example. For example, the keyword receiving unit 6 may accept a search keyword via an input device (not shown) included in the related score calculation system 1. Further, the output unit 8 may output (display) the search result to a display device (not shown) included in the related score calculation system 1.

Similar to the collection unit 2, the keyword reception unit 6 and the output unit 8 are realized by, for example, a CPU of a computer operating according to a related score calculation program and a communication interface of the computer. Further, the search unit 7 is realized by the CPU of the computer that operates according to the related score calculation program. Further, the keyword reception unit 6, the search unit 7, the output unit 8, and other components may be realized by separate hardware.

Next, an example of a process in which the related score calculation system 1 accepts a word as a search keyword and searches for a user name of a user corresponding to the word will be described.

First, the keyword reception unit 6 accepts a word from a searcher as a search keyword.

Next, the search unit 7 associates the word corresponding to the search keyword with the word corresponding to the search keyword from the set of the user name, the word, and the association score stored in the storage unit 5, and the association of the threshold value (for example, 0.5) or more. Identify the set that includes the score, and identify the user names included in that set as search results.

For example, suppose that the keyword reception unit 6 accepts the word "artificial intelligence" as a search keyword. Further, it is assumed that the above threshold value is 0.5. In this case, the search unit 7 identifies a group including the word "artificial intelligence" and a related score of 0.5 or more from the plurality of sets illustrated in FIG. In this example, the first set shown in FIG. 6 is specified. The search unit 7 obtains the user name "Yamada" included in the specified group as a search result. The output unit 8 outputs the search result. If there are a plurality of pairs including the word "artificial intelligence" and a related score of 0.5 or more, the search unit 7 identifies all the pairs and searches for the user name obtained from each pair. And. Therefore, the number of user names obtained as a search result is not limited to one. The output unit 8 may output a plurality of user names obtained as search results in descending order of the related score.

Since the user name can be searched from the word in this way, the searcher can easily know the user name of the user who is strongly involved in the field, technology, project, etc. represented by the word designated as the search keyword.

Next, an example of a process in which the related score calculation system 1 accepts a user name as a keyword and searches for a word corresponding to the user name will be described.

First, the keyword reception unit 6 accepts a user name as a search keyword from a searcher.

Next, the search unit 7 has a user name corresponding to the search keyword and a threshold value (for example, 0.5) or more from the set of the user name, the word, and the related score stored in the storage unit 5. Identify the set that contains the relevant score, and identify the words contained in that set as search results. At this time, if there are a plurality of pairs including the user name corresponding to the search keyword and the related score equal to or higher than the threshold value, the search unit 7 identifies all the pairs, and the word obtained from each pair is used as the search result. do. Therefore, the number of words obtained as a search result is not limited to one. The output unit 8 may output a plurality of words obtained as search results in descending order of the related score.

For example, it is assumed that the keyword reception unit 6 accepts the user name "Yamada" as a search keyword. Further, it is assumed that the above threshold value is 0.5. In this case, the search unit 7 identifies a group including the user name "Yamada" and a related score of 0.5 or more from the plurality of groups illustrated in FIG. In this example, the first set, the third set, and the fourth set shown in FIG. 6 are specified. The search unit 7 obtains the words included in each specified set as the search result. That is, the search unit 7 obtains "artificial intelligence", "machine learning", and "deep learning" as search results. The output unit 8 outputs the search result.

In this way, since the word can be searched from the user name, the searcher can easily estimate the field, technology, etc. that the person having the user name specified as the search keyword is familiar with, or that the person participated. It is possible to easily estimate a certain project or the like.

The above threshold value “0.5” is an example, and the threshold value does not have to be 0.5. Further, the threshold value may be set according to the calculation method of the related score.

Embodiment 2.
FIG. 7 is a block diagram showing a configuration example of the related score calculation system according to the second embodiment of the present invention. The same components as those shown in the related score calculation system of the first embodiment (see FIG. 1) and the modified example of the first embodiment (see FIG. 5) are referred to as reference numerals shown in FIGS. 1 and 5. The same reference numerals are given, and the description thereof will be omitted.

In the second embodiment, the related score calculation system 1 includes a collection unit 2, a word extraction unit 3, a score calculation unit 4, a storage unit 5, a first table generation unit 11, and a second table generation. A unit 12, a keyword reception unit 6, a search unit 17, and an output unit 8 are provided.

The collection unit 2, the word extraction unit 3, and the score calculation unit 4 are the same as those of the first embodiment (see FIG. 1) and the modified examples of the first embodiment (see FIG. 5). be.

Further, the keyword receiving unit 6 and the output unit 8 are the same as the respective elements shown in the modified example (see FIG. 5) of the first embodiment.

The storage unit 5 is the same as the storage unit 5 in the first embodiment (see FIG. 1) and the modified example of the first embodiment (see FIG. 5). However, in the present embodiment, the storage unit 5 not only stores a plurality of sets of the user name, the word, and the related score, but also stores the first table 21 and the second table 22, which will be described later. Hereinafter, a set of a set of a user name, a word, and a related score (for example, a plurality of sets illustrated in FIG. 6) stored in the storage unit 5 by the score calculation unit 4 is referred to as a related score calculation result 20.

Further, in the present embodiment, the case where the score calculation unit 4 calculates the related score by the above-mentioned third calculation method will be described as an example. However, the score calculation unit 4 may calculate the related score by the above-mentioned first calculation method or the second calculation method.

In the following description, it is assumed that the score calculation unit 4 has already calculated the related score and the storage unit 5 stores the related score calculation result 20. FIG. 8 is a schematic diagram showing an example of the related score calculation result 20 (a set of a set of a user name, a word, and a related score). Here, a case where the related score calculation result 20 illustrated in FIG. 8 is stored in the storage unit 5 will be described as an example.

The first table generation unit 11 generates the first table 21 based on the related score calculation result 20, and stores it in the storage unit 5. The first table 21 is a table describing the relationship between the user ID, the word, and the related score. More specifically, in the first table 21, the user names included in the related score calculation result 20 are arranged on one of the vertical axis and the horizontal axis, and are included in the related score calculation result 20. It is a table in which words are arranged on the other axis and the association score of the user having the user name and the word is described in the column where the user name and the word intersect.

In this example, when the first table generation unit 11 generates the first table, the case where the user names are arranged on the vertical axis and the words are arranged on the horizontal axis will be described as an example. The generation unit 11 may arrange the user names on the horizontal axis and the words on the vertical axis.

Further, when arranging the user names along the axis, the first table generation unit 11 arranges the user names of the same user without duplication. For example, in the related score calculation result 20 shown in FIG. 8, the user name “Yamada” is also included in the first group, and the user name “Yamada” is also included in the second group. This user name "Yamada" is a user name of the same user. Therefore, when arranging the user names along the axis, the first table generation unit 11 may arrange the user names "Yamada" once.

Similarly, when arranging the words along the axis, the first table generation unit 11 arranges the same words without duplication. For example, in the related score calculation result 20 shown in FIG. 8, the word "artificial intelligence" is included in a plurality of sets. However, the first table generation unit 11 may arrange the word "artificial intelligence" once when arranging the words along the axis.

An example of the first table generated by the first table generation unit 11 is shown in FIG. The first table generation unit 11 arranges the user names (“Yamada”, “Suzuki”, “Tanaka”, etc.) included in the related score calculation result 20 along the direction of the vertical axis (see FIG. 9). .. Further, the first table generation unit 11 sets the words (“artificial intelligence”, “company A”, “machine learning”, “deep learning”, etc.) included in the related score calculation result 20 in the direction of the horizontal axis. Arrange along (see FIG. 9).

Then, the first table generation unit 11 describes the association score between the user having the user name and the word in the column where the user name and the word intersect. For example, in the example shown in FIG. 8, the association score between the user name “Yamada” and the word “artificial intelligence” is “0.80”. Therefore, the first table generation unit 11 describes "0.80" in the column where "Yamada" and "artificial intelligence" intersect in the first table 21 (see FIG. 9). Further, for example, in the example shown in FIG. 8, the related score of the user name “Yamada” and the word “Company A” is “0.10”. Therefore, the first table generation unit 11 describes "0.10" in the column where "Yamada" and "Company A" intersect in the first table 21. The first table generation unit 11 similarly describes the related score for each column where the user name and the word intersect.

The first table generation unit 11 stores the first table 21 generated as described above in the storage unit 5.

The second table generation unit 12 generates a second table 22 based on the first table 21, and stores it in the storage unit 5. The second table is a table describing the degree of relevance representing the strength of the relevance between words calculated based on the first table. More specifically, in the second table 22, words are arranged on both the vertical axis and the horizontal axis, and in the column where the words intersect, the strength of the relationship between the words calculated based on the first table 21 is strong. It is a table that describes the degree of relevance that represents the value. As described above, in the present invention, the index value indicating the strength of the relationship between the user and the word is referred to as "relationship score", and the index value indicating the strength of the relationship between words is referred to as "relevance degree". Distinguish between two types of index values.

FIG. 10 is a schematic diagram showing an example of the second table. In this example, a case where the second table generation unit 12 arranges words in the same order as the first table 21 on each of the vertical axis and the horizontal axis will be described as an example. For example, in the first table illustrated in FIG. 9, words are arranged in the order of "artificial intelligence", "company A", "machine learning", "deep learning", and so on. The second table generation unit 12 arranges words in the same order on the vertical axis and the horizontal axis of the second table (see FIG. 10).

Then, the second table generation unit 12 calculates the degree of relevance indicating the strength of the relevance between the words for each combination of one word and one word, and puts the relevance in the column where the words intersect. Describe the degree of relevance. The second table generation unit 12 calculates the degree of relevance even for the same words. For example, the second table generation unit 12 also calculates the degree of relevance between "artificial intelligence" and "artificial intelligence".

Here, the degree of relevance between words will be described. First, in the first power strip 21 illustrated in FIG. 9, a case where two words "artificial intelligence" and "machine learning" are focused on will be described.

The association scores of "artificial intelligence" and individual users are arranged in the order of user names in the first table 21 as follows.

0.80, 0.20, 0.60, 0.43, ...

Further, the related scores of "machine learning" and each user are arranged in the order of user names in the first table 21 as follows.

0.79, 0.22, 0.58, 0.45, ...

It can be said that the tendency of the change of the related score in the above-mentioned sequence of the related score is similar. In this case, the degree of relevance between "artificial intelligence" and "machine learning" is high.

Next, a case where the two words "artificial intelligence" and "company A" are focused on will be described.

As described above, the related scores of "artificial intelligence" and individual users are arranged in the order of user names in the first table 21 as follows.

0.80, 0.20, 0.60, 0.43, ...

Further, the related scores of "Company A" and each user are arranged in the order of user names in the first table 21 as follows.

0.10, 0.40, 0.35, 0.05, ...

It can be said that the tendency of the change of the related score in the above-mentioned sequence of related scores is not similar. In this case, the degree of relevance between "artificial intelligence" and "company A" is low.

The second table generation unit 12 may calculate the correlation coefficient between words as the degree of relevance between words. Here, as relevance of a word w ₁ and word w _2, it will be described for calculating a correlation coefficient of words w ₁ and word w _2. Correlation coefficient of words w ₁ and word w _2, more specifically, the arrangement of words w ₁ and related scores for individual users, the correlation function of the sequence of words w ₂ and associated scores for individual users be.

The sequence of related scores (numerical values) when the related scores of one word and each user are arranged in the order of user names in the first table 21 is referred to as a sequence of the words. For example, in the example shown in FIG. 9, the series of "artificial intelligence" is as follows.

0.80, 0.20, 0.60, 0.43, ...

It is assumed that the sequence of the word w _{1 is (x 1} , x ₂ , ..., X _n). Then, let x be this series. In the example shown in FIG. 9, the word sequence is a series of words arranged in the vertical direction, but here, for convenience, (x ₁ , x ₂ , ..., X _n ) and horizontally. Shown side by side. This is also true for the word w ₂ described next.

Further, it is assumed that the sequence of _{words w 2 is (y 1} , y ₂ , ..., Y _n). Then, let y be this series.

As described above, the number of related scores belonging to the series is n.

Second table generator 12, a relevance of words w ₁ and word w _2, may be calculated a correlation coefficient of x and y. Let r be the correlation coefficient between x and y. The second table generation unit 12 calculates the correlation coefficient r between x and y by the calculation of the following equation (4).

In equation (4), s _xy is a covariance of x and y. Further, s _x is the standard deviation of x, and _sy is the standard deviation of y. x _i is the i-th association score in x. y _i is the i-th association score in y.

は、ｘの平均値である。

Is the average value of x.

は、ｙの平均値である。

Is the average value of y.

The second table generation unit 12 calculates the correlation coefficient by the calculation of the equation (4) for each combination of one word and one word, and uses the correlation coefficient as the degree of relevance as the second table. 22 is described. When two words in a combination are different words, the degree of relevance of the two words is described in two places in the second table 22. For example, in the example shown in FIG. 10, the degree of relevance between "artificial intelligence" and "company A" is described in the first row and the second column and the second row and the first column, respectively.

The second table generation unit 12 stores the second table 22 generated as described above in the storage unit 5.

The related score calculation system 1 of the second embodiment accepts a word as a search keyword and searches for the user name of the user corresponding to the word. Alternatively, the related score calculation system 1 of the second embodiment accepts the user name as a search keyword and searches for a word corresponding to the user name. Further, the related score calculation system 1 of the second embodiment may be capable of executing each of the above two types of searches.

When searching for a user name of a user corresponding to a word designated as a searcher, the keyword receiving unit 6 accepts the word from the searcher as a search keyword. Then, the search unit 17 searches for the user name corresponding to the word corresponding to the search keyword based on the first table 21 and the second table 22.

Further, when searching for a word corresponding to the user name designated by the searcher, the keyword receiving unit 6 accepts the user name from the searcher as a search keyword. Then, the search unit 17 searches for a word corresponding to the user name corresponding to the search keyword based on the first table 21 and the second table 22.

In the second embodiment, the collection unit 2, the keyword reception unit 6, and the output unit 8 are realized by the CPU of the computer operating according to the related score calculation program and the communication interface of the computer. For example, the CPU reads a related score calculation program from a program recording medium such as a program storage device of a computer, and operates as a collecting unit 2, a keyword receiving unit 6, and an output unit 8 using a communication interface according to the related score calculation program. Just do it. Further, the word extraction unit 3, the score calculation unit 4, the first table generation unit 11, the second table generation unit 12, and the search unit 17 are also realized by, for example, the CPU of the above computer operating according to the related score calculation program. To. That is, as described above, the CPU that has read the related score calculation program performs the word extraction unit 3, the score calculation unit 4, the first table generation unit 11, the second table generation unit 12, and the search according to the related score calculation program. It may operate as a unit 17. Further, the collection unit 2, the keyword reception unit 6, the output unit 8, the word extraction unit 3, the score calculation unit 4, the first table generation unit 11, the second table generation unit 12, and the search unit 17 have separate hardware. May be realized by.

FIG. 11 is a flowchart showing an example of the processing progress of the second embodiment. Detailed description of the operation similar to the operation described in the first embodiment and the operation already described in the second embodiment will be omitted.

Steps S1 to S3 are the same as steps S1 to S3 (see FIG. 4) in the first embodiment, and the description thereof will be omitted.

After step S3, the first table generation unit 11 generates the first table 21 (see FIG. 9) based on the related score calculation result 20, and stores the first table 21 in the storage unit 5. (Step S4). Since the operation of generating the first table 21 has already been described, the description thereof will be omitted here.

Next, the second table generation unit 12 generates a second table 22 (see FIG. 10) based on the first table 21, and stores the second table 22 in the storage unit 5 (step S5). ). Since the operation of generating the second table 22 has already been described, the description thereof will be omitted here.

After the first table 21 and the second table 22 are generated, the keyword receiving unit 6 receives the search keyword from the searcher (step S6). The keyword reception unit 6 may accept a word as a search keyword. Further, the keyword receiving unit 6 may accept a user name as a search keyword.

Next, the search unit 17 obtains search results according to the search keyword based on the first table 21 and the second table 22 (step S7). When the search keyword is a word, the search unit 17 searches for a user name corresponding to the word based on the first table 21 and the second table 22. When the search keyword is a user name, the search unit 17 searches for a word corresponding to the user name based on the first table 21 and the second table 22. The details of the operation of step S7 will be described later.

After step S7, the output unit 8 outputs the search result (step S8). The mode in which the output unit 8 outputs the search result is the same as the modification of the first embodiment.

Next, the operation of step S7 will be described. First, in step S6, a case where the keyword receiving unit 6 accepts a word as a selection keyword and the search unit 17 searches for a user name corresponding to the word will be described. Hereinafter, the word corresponding to the search keyword is referred to as a keyword word.

The search unit 17 sequentially selects individual user names, and calculates a search score for the selected user name (hereinafter referred to as a selected user name) and a keyword word. The search score is an index value indicating the strength of the association between the user having the selected user name and the word, but it is calculated using the association score already described, and the calculation method is different from the association score. Therefore, in the following description, the term search score is used to distinguish it from the related score.

It is assumed that the search unit 17 has selected one selected user name. The operation of the search unit 17 to calculate the search score of the selected user name and the keyword word will be described. FIG. 12 is a flowchart showing an example of a process of calculating the search score of the selected user and the keyword word.

First, the search unit 17 initializes the value of the search score of the selected user name to 0 (step S11).

Next, the search unit 17 selects one word that has not yet been selected in step S12 from the words arranged on the axis of the first table (may be the axis of the second table) (step). S12). The word selected in step S12 is hereinafter referred to as a selected word. The selected word may be the same as the keyword word.

Next, the search unit 17 calculates the product of the degree of relevance between the keyword word and the selected word and the relevance score between the selected user name and the selected word (step S13). The search unit 17 may read the relevance degree used in step S13 from the second table and the relevance score used in step S13 from the first table.

Next, the search unit 17 adds the product calculated in step S13 to the search score (step S14).

Next, the search unit 17 determines whether or not there is a word not selected in step S12 (step S15). If there is an unselected word (Yes in step S15), the search unit 17 repeats the processes after step S12.

When there is no unselected word (No in step S15), the search unit 17 determines the value of the search score at that time as the search score of the selected user name and the keyword word, and ends the process.

The above search score calculation process can be expressed by the following equation (5).

In equation (5), the word i means the i-th selected selected word.

For example, it is assumed that the first table 21 shown in FIG. 9 and the second table 22 shown in FIG. 10 are stored in the storage unit 5. Then, it is assumed that the keyword word (word corresponding to the search keyword) is "artificial intelligence" and the selected user name is "Yamada". In this case, the search unit 17 calculates the search score of the selected user name "Yamada" and the keyword word "artificial intelligence" by the following formula (see FIGS. 9 and 10).

Search score = 1.00 x 0.80 + 0.07 x 0.10 + 0.87 x 0.79 + 0.79 x 0.82 + ...

Further, it is assumed that the selected user name is "Suzuki". In this case, the search unit 17 calculates the search score of the selected user name "Suzuki" and the keyword word "artificial intelligence" by the following formula (see FIGS. 9 and 10).

Search score = 1.00 x 0.20 + 0.07 x 0.40 + 0.87 x 0.22 + 0.79 x 0.18 + ...

The search unit 17 obtains a search score by the above processing for each user name described in the first table 21. Then, the search unit 17 obtains a user name whose search score is equal to or higher than the threshold value as a search result. Therefore, the number of user names obtained as a search result may be plural. The output unit 8 outputs the user name obtained as the search result. The output unit 8 may output a plurality of user names obtained as search results in descending order of search score.

This search method also uses a second table showing the degree of relevance between words. Therefore, the user name of a user who is strongly related to another word which is highly related to the word specified by the searcher as the search keyword can also be obtained as the search result.

For example, in the company to which each user belongs, "Red Rockets" and "Green Rockets" are product names of important products, and the two products are strongly related. In this case, according to the above search method, even if "Red Rockets" is used as the search keyword, not only the user name of the user who is strongly related to "Red Rockets" but also the user name related to "Red Rockets". The user name of a user who is strongly related to a certain "Green Rockets" can also be obtained as a search result. Therefore, it is possible to search a wide range of user names based on the words specified by the searcher as the search keyword.

Further, in the present embodiment, the second table is generated by using the words included in the operation log collected from the PC 91 provided in the company. Therefore, words often used by the company such as "Red Rockets" and "Green Rockets" as exemplified above can be included in the second table. If the first table and the second table are manually created, not only is it time-consuming, but also words and the like used by the organization are likely to leak from the first table and the second table. Therefore, it is possible to easily create the first table and the second table without leaking the words used by the organization, and as described above, a wide range of user names are used based on the words specified as the search keywords. You can search.

Next, in step S6, a case where the keyword receiving unit 6 accepts a user name as a selection keyword and the search unit 17 searches for a word corresponding to the user name will be described. Hereinafter, the user name corresponding to the search keyword is referred to as a keyword user name.

The search unit 17 sequentially selects individual words and calculates a search score for the selected word and the keyword user name. This selected word is referred to as a score calculation target word. Further, even after selecting one score calculation target word, the words are separately sequentially selected as described later (see step S22 in FIG. 13 described later). The word selected in step S22 described later is referred to as a selected word.

It is assumed that the search unit 17 has selected one score calculation target word. The operation of the search unit 17 to calculate the search score of the score calculation target word and the keyword user name will be described below. FIG. 13 is a flowchart showing an example of a process of calculating the search score of the score calculation target word and the keyword user name.

First, the search unit 17 initializes the value of the search score of the score calculation target word to 0 (step S21).

Next, the search unit 17 selects one word that has not yet been selected in step S22 from the words arranged on the axis of the first table (may be the axis of the second table) (step). S22). As described above, the word selected in step S22 is referred to as a selected word. The selected word may be the same as the score calculation target word.

Next, the search unit 17 calculates the product of the relevance score between the keyword user name and the selected word and the relevance degree between the score calculation target word and the selected word (step S23). The search unit 17 may read the association score used in step S23 from the first table and the association degree used in step S23 from the second table.

Next, the search unit 17 adds the product calculated in step S23 to the search score (step S24).

Next, the search unit 17 determines whether or not there is a word not selected in step S22 (step S25). If there is an unselected word (Yes in step S25), the search unit 17 repeats the processes after step S22.

When there is no unselected word (No in step S25), the search unit 17 determines the value of the search score at that time as the search score of the word to be scored and the keyword user name, and ends the process.

The above search score calculation process can be expressed by the following equation (6).

In equation (6), the word i means the i-th selected selected word.

For example, it is assumed that the first table 21 shown in FIG. 9 and the second table 22 shown in FIG. 10 are stored in the storage unit 5. Then, it is assumed that the keyword user name (user name corresponding to the search keyword) is "Yamada" and the score calculation target word is "artificial intelligence". In this case, the search unit 17 calculates the search score of the score calculation target word "artificial intelligence" and the keyword user name "Yamada" by the following formula (see FIGS. 9 and 10).

Search score = 0.08 x 1.00 + 0.10 x 0.07 + 0.79 x 0.87 + 0.82 x 0.79 + ...

Further, it is assumed that the word for which the score is calculated is "Company A". In this case, the search unit 17 calculates the search score for the score calculation target word "Company A" and the keyword user name "Yamada" by the following formula (see FIGS. 9 and 10).

Search score = 0.08 x 0.07 + 0.10 x 1.00 + 0.79 x 0.09 + 0.82 x 0.11 + ...

The search unit 17 obtains a search score by the above processing for each score calculation target word to be sequentially selected. Then, the search unit 17 obtains a word whose search score is equal to or higher than the threshold value as a search result. Therefore, the number of words obtained as a search result may be plural. The output unit 8 outputs the word obtained as the search result. The output unit 8 may output a plurality of words obtained as search results in descending order of search score.

In the search method for searching for a word from a user name as described above, a second table showing the degree of relevance between words is also used. Therefore, not only a word having a high degree of relevance to the user name represented by the keyword user name designated by the searcher but also another word having a strong relevance to the word can be obtained as a search result.

For example, as in the above example, in the company to which each user belongs, "Red Rockets" and "Green Rockets" are product names of important products, and it is assumed that the two products are strongly related. Then, it is assumed that the user "Yamada" and the word "Red Rockets" are strongly related. In this case, if the searcher specifies "Yamada" as the keyword user name, not only the word "Red Rockets" that is strongly related to "Yamada" but also the word "Green Rockets" that is strongly related to the word "Red Rockets" will be searched. Can be obtained as. Since "Red Rockets" and "Green Rockets" are strongly related, "Yamada", which is strongly related to "Red Rockets", is considered to be strongly related to "Green Rockets". According to the above method, it is possible to search a wide range of words that are considered to be strongly related to the user indicated by the user name specified as the search keyword.

Further, as described above, the first table and the second table can be easily created without leaking the words used by the organization.

As described above, in the present embodiment, when the keyword receiving unit 6 accepts a word as a selection keyword and the search unit 17 searches for a user name corresponding to the word, the user name can be widely searched. Further, when the keyword receiving unit 6 accepts a user name as a selection keyword and the search unit 17 searches for a word corresponding to the user name, the word can be widely searched. In addition, the first table and the second table can be easily created without leaking the words used by the organization.

Embodiment 3.
FIG. 14 is a block diagram showing a configuration example of the related score calculation system according to the third embodiment of the present invention. In addition to the components included in the related score calculation system of the second embodiment (see FIG. 7), the related score calculation system of the third embodiment further includes a clustering unit 31, a cluster output unit 32, and excluded words. It is equipped with a reception unit 33. In FIG. 14, the same elements as those shown in FIG. 7 are designated by the same reference numerals as those in FIG. 7, and the description thereof will be omitted. The first table generation unit 11 and the second table generation unit 12 perform the operations described later in addition to the operations described in the second embodiment, respectively.

The clustering unit 31 reads the second table 22 (see, for example, FIG. 10) from the storage unit 5, and clusters the words described in the second table 22 based on the second table 22. conduct. The clustering unit 31 clusters words by, for example, a clustering method such as a k-means method or a hierarchical clustering algorithm. In a clustering method such as the k-means method or a hierarchical clustering algorithm, clustering can be performed on words arranged on the axis of the second table 22 using the second table 22 as input data. The clustering unit 31 assigns, for example, a cluster number to each cluster as cluster identification information, and stores the relationship between the cluster and the word belonging to the cluster in the storage unit 5 as the clustering result 23.

The cluster output unit 32 receives, for example, a clustering result output request from a terminal device (not shown) of an administrator of the related score calculation system 1 (hereinafter, simply referred to as an administrator) via a communication network. In response to the output request, the screen information of the screen showing the clustering result is transmitted to the terminal device (not shown). FIG. 15 is a schematic diagram showing an example of a screen showing a clustering result. On the screen showing the clustering result, for example, the cluster number and each word belonging to the cluster are displayed for each cluster. In addition, a check box 41 is displayed together with each cluster number and each word. As shown in FIG. 15, the number of words belonging to each cluster may be different. Further, the screen showing the clustering result includes a confirmation button 42.

When the cluster output unit 32 receives a clustering result output request from the administrator's terminal device via the communication network, the cluster output unit 32 reads the clustering result 23 from the storage unit 5. Then, the cluster output unit 32 displays the cluster number and the word belonging to the cluster indicated by the cluster number based on the clustering result 23, displays each cluster number and the check box 41 together with each word, and further. The screen information of the screen including the confirmation button 42 (for example, the screen illustrated in FIG. 15) is generated. Then, the cluster output unit 32 transmits the screen information to the terminal device of the administrator via the communication network.

When the administrator's terminal device receives the screen information from the cluster output unit 32, the administrator's terminal device displays, for example, the screen illustrated in FIG. 15 based on the screen information.

The administrator confirms the screen illustrated in FIG. 15 (the screen displaying the clustering result of the word), and checks the check box 41 corresponding to the word determined to be excluded by the administrator. If the administrator determines that all words belonging to one cluster should be excluded, the check box 41 corresponding to that cluster is checked. The fact that the check box 41 corresponding to a word is checked means that it is determined that the word should be excluded. Further, the fact that the check box 41 corresponding to the cluster is checked means that it is determined that all the words belonging to the cluster should be excluded. Further, here, "to be excluded" means that the word should be excluded from the words arranged on the horizontal axis of the first table and each axis of the second table.

The confirmation button 42 is a button for inputting that the determination as to whether or not to exclude each word has been completed. When the confirmation button 42 is clicked by the administrator, the terminal device of the administrator determines a word that the administrator has determined to be excluded according to which check box 41 is checked, and the administrator determines. Words determined to be excluded are transmitted to the related score calculation system 1.

The exclusion target word reception unit 33 of the related score calculation system 1 receives the words transmitted by the terminal device of the administrator (words determined by the administrator to be excluded) via the communication network. The number of words received by the exclusion target word reception unit 33 is not limited to one. It can be said that the exclusion target word reception unit 33 accepts the designation of words to be excluded from the words arranged on the horizontal axis of the first table and each axis of the second table.

The exclusion target word reception unit 33 notifies the first table generation unit 11 of the words (words to be excluded) received from the terminal device of the administrator. Upon receiving the notification of the word, the first table generation unit 11 excludes the notified word from the words arranged on the horizontal axis of the already generated first table 21, and re-uses the first table 21. Generate. Then, the first table generation unit 11 replaces the first table 21 stored in the storage unit 5 with the regenerated first table 21.

When the first table generation unit 11 regenerates the first table 21, the second table generation unit 12 regenerates the second table based on the newly generated first table 21. At this time, the second table generation unit 12 excludes the words received by the exclusion target word reception unit 33 from the words arranged on each axis to generate the second table. Then, the second table generation unit 12 replaces the second table 22 stored in the storage unit 5 with the second table 22 generated again.

The operation of regenerating the first table 21 and the operation of regenerating the second table 22 are the first described in the second embodiment, except that the number of words arranged on the axis is reduced. It is the same as the operation of generating the table 21 and the operation of generating the second table 22.

The cluster output unit 32 and the exclusion target word reception unit 33 are realized, for example, by the CPU of a computer operating according to the related score calculation program and the communication interface of the computer. Further, the clustering unit 31 is realized by the CPU of the computer that operates according to the related score calculation program.

According to the present embodiment, words determined by the administrator to be excluded from the first table and the second table can be excluded from the first table and the second table. As a result, for example, when searching for a word using a user name as a search keyword, the word specified by the administrator as a word to be excluded can be prevented from being included in the search result.

Here are some examples of words that the administrator decides should be excluded. For example, the user name "Yamada" may be included in the file name newly created or browsed by the user "Yamada". In that case, when searching for a word using the user name "Yamada" as a search keyword, the word "Yamada" may be included in the search result. However, when searching for a word using the user name "Yamada" as a search keyword, even if the search result includes the word "Yamada", it does not make much sense to the searcher.

Therefore, for example, when the screen illustrated in FIG. 15 is displayed, the administrator determines that the word "Yamada", which is clearly considered to be the same character string as the user name, should be excluded, and FIG. 15 Check the check box 41 corresponding to the word "Yamada" shown in 1 and click the confirm button 42. As a result, the first table generation unit 11 generates a new first table excluding "Yamada" from the sequence of words on the horizontal axis. Subsequently, the second table generation unit 12 generates a new second table by excluding "Yamada" from the sequence of words on the vertical axis and the horizontal axis. As a result, for example, when searching for a word using the user name "Yamada" as a search keyword, it is possible to prevent the word "Yamada" from being included in the search results.

The cluster output unit 32 may display a screen showing the clustering result on a display device (not shown) included in the related score calculation system 1. Further, the exclusion target word receiving unit 33 operates on the screen displayed on the display device by the input device (not shown) included in the related score calculation system 1 (input of a check in the check box 41 and the confirmation button 42). (Click) may be accepted, and the designation of words to be excluded may be accepted according to the operation.

Embodiment 4.
In the first to third embodiments, the collection unit 2 collects the operation log, the word extraction unit 3 extracts words from each file name described in each operation log, and the score calculation unit 4 Shown the case where the association score is calculated for each combination of each user who operated the file and each extracted word.

In the fourth embodiment, a case where the collecting unit collects schedule information instead of the operation log of the file will be described as an example.

FIG. 16 is a block diagram showing a configuration example of the related score calculation system of the fourth embodiment. The related score calculation system 1 shown in FIG. 16 replaces the collection unit 2, the word extraction unit 3, and the score calculation unit 4 in the first embodiment with the collection unit 52, the word extraction unit 53, and the score calculation unit 54. be. The storage unit 5 is the same as the storage unit 5 in the first embodiment.

Further, the related score calculation system 1 shown in FIG. 16 is connected to the schedule management server 61 that holds the schedule information of each person belonging to the organization via the communication network 10.

The collection unit 52 collects the schedule information of each person belonging to the organization stored in the schedule management server 61 from the schedule management server 61. The schedule information may be stored in individual PCs (not shown in FIG. 16). In this case, the collecting unit 52 may collect the schedule information of each person belonging to the organization from the individual PCs.

Hereinafter, the identification information of the person belonging to the organization is referred to as the person identification information. Further, a case where a person's name such as "Yamada" is used as the person identification information will be described as an example.

FIG. 17 is a schematic diagram showing an example of schedule information. The schedule information is described, for example, as illustrated in FIG. 17, in which the person identification information of a person belonging to the organization, the event name, and the time zone in which the person was involved in the event having the event name are associated with each other. .. The time zone is represented by the start date and time and the end date and time.

In the first schedule information shown in Fig. 17, a person named "Yamada" was involved in the "Artificial Intelligence Development Conference" from 10:00 on August 17, 2017 to 12:00 on the same day (in other words, attended). It represents that. Hereinafter, the time when a person is involved in a certain event is referred to as the event participation time. The event participation time for one event is the time obtained by subtracting the start date and time from the end date and time described in association with the event.

The word extraction unit 53 extracts words included in the event name by performing morphological analysis on each event name described in each schedule information collected by the collection unit 52.

However, the word extraction unit 53 does not extract the same word in duplicate. For example, when the word extraction unit 53 has already extracted the word "artificial intelligence", the word "artificial intelligence" extracted from the second time onward is ignored. This point is the same as the word extraction unit 3 already described.

For example, the word extraction unit 53 extracts words such as "artificial intelligence", "development", and "meeting" by performing morphological analysis on the event name "artificial intelligence development meeting".

The word extraction unit 53 obtains a set of words by performing this process for each event name described in each schedule information. Words that belong to this set are different from each other because the same word is not extracted more than once.

The score calculation unit 54 calculates a related score indicating the strength of the relationship between the person and the word for each combination of each person belonging to the organization and each word extracted by the word extraction unit 53. This association score is the same as the association score in the first embodiment to the third embodiment. However, in the fourth embodiment, the number of key touches is not used as the related score. That is, the word extraction unit 53 does not calculate the related score by the second calculation method described in the first embodiment.

Hereinafter, two types of methods will be described as methods for calculating the related score in the fourth embodiment. In any of the two types of methods shown below, the score calculation unit 54 calculates a related score for each combination of a person and a word and stores it in the storage unit 5.

The first calculation method of the association score in the fourth embodiment is the same method as the first calculation method of the association score in the first embodiment. However, the event participation time is used instead of the file operation time.

In the first calculation method of the association score in the fourth embodiment, the word W is used as an event as the association score of one person (hereinafter referred to as person H) and one word (hereinafter referred to as word W). This is a method of calculating the total event participation time of person H for each event included in the name. It can be said that the longer the event participation time, the stronger the relationship between the person H and the word W, and the shorter the event participation time, the weaker the relationship between the person H and the word W. Therefore, the event participation time can be used as a related score.

In the first calculation method, the score calculation unit 54 calculates the total event participation time of the person H for each event including the word W in the event name, and the total is used as the related score between the person H and the word W.

That is, the score calculation unit 54 includes the word W in the event name, calculates the event participation time for each event in which the person H participates, and sets the total as the related score between the person H and the word W.

The second calculation method of the association score in the fourth embodiment is the same method as the third calculation method of the association score in the first embodiment. However, the event participation time is used instead of the file operation time.

The second method of calculating the association score in the present embodiment is a method of calculating the association score of one person (person H) and one word (word W) based on the two ratios described below. Of the two proportions, describing one ratio of the Q _1, mark the other ratio between Q _2.

Q ₁ is, for all of the people of the events of participation time the sum of the organization for each event, including the word W in the event name, there is a percentage of the sum of the events participation time of people H for each event, including the word W in the event name .. That is, Q ₁ is represented by the following equation (7).

Q ₂ is, in the case of focusing on individual words, to the sum of events participation time of people H for each event, including the word that focuses on the event name, event participation time of people H for each event, including the word W in the event name It is the ratio of the total of. That is, Q2 is represented by the following equation (8).

Q ₁ will be explained. The sum of the event participation times of the person H for each event including the word W in the event name (the numerator on the right side of the equation (7)) corresponds to the association score calculated by the first calculation method described above. That is, the score calculation unit 54 may calculate the total event participation time of the person H for each event including the word W in the event name by the method described in the first calculation method described above.

The sum of the event participation times of all the people in the organization for each event including the word W in the event name (the denominator on the right side of the equation (7)) will be described. The score calculation unit 54 also calculates the total event participation time of the first person in the organization for each event including the word W in the event name by the method described in the first calculation method described above. Similarly, the score calculation unit 54 also calculates the total event participation time of the second person in the organization for each event including the word W in the event name by the method described in the first calculation method described above. Similarly, the score calculation unit 54 calculates the total event participation time of a person for each event including the word W in the event name for each person belonging to the organization. Further, the score calculation unit 54 calculates the sum of "the sum of the event participation time of a person for each event including the word W in the event name" calculated for each person belonging to the organization. This value corresponds to the sum of the event participation times of all people in the organization for each event including the word W in the event name.

Score calculation unit 54, the molecules of the right-hand side of formula (7) calculated as described above, by using a denominator, the calculation of equation (7), and calculates the Q _1.

Next, the _{Q 2} will be described. The molecule on the right side of the formula (8) is the same as the molecule on the right side of the formula (7). Therefore, the score calculation unit 54 may calculate the total event participation time of the person H for each event including the word W in the event name by the method described in the first calculation method described above.

The sum of the event participation time of the person H for each event including the focused word in the event name (denominator on the right side of the equation (8)) when focusing on each word will be described. The score calculation unit 54 pays attention to the individual words extracted by the word extraction unit 53 (in other words, each individual word is selected one by one). Then, the score calculation unit 54 calculates the total event participation time of the person H for each event including the word of interest (selected word) in the event name by the method described in the first calculation method described above. The score calculation unit 54 focuses on the next word (in other words, selects the next word), and sums up the event participation time of the person H for each event including the focused word (selected word) in the event name. , Calculate by the method described in the above-mentioned first calculation method. In this way, the score calculation unit 54 calculates the total event participation time of the person H for each event including the word in the event name for each word. Then, the score calculation unit 54 calculates the total of "the total of the event participation time of the person H for each event including the word in the event name" calculated for each word. This value corresponds to the denominator of the equation (8). "When focusing on individual words, the sum of the event participation times of person H for each event that includes the focused word in the event name (the denominator on the right side of equation (8))" is "each including the word in the event name. It can be said that it is the total sum of the total sum of the event participation times of the person H for the event for each word.

Score calculation unit 54, the molecules of the right-hand side of Equation (8) calculated as described above, by using a denominator, the calculation of equation (8), and calculates the Q _2.

Score calculation unit _54, after obtaining the Q 1, _{Q 2,} the relevance score of human H and words W, is calculated by the equation (9) below.

Related score = Q ₁ x log (Q ₂ ) ・ ・ ・ (9)

When the related score is calculated by the second calculation method in the fourth embodiment, the same effect as when the related score is calculated by the third calculation method described in the first embodiment can be obtained.

The collecting unit 52 is realized by, for example, a CPU of a computer operating according to a related score calculation program and a communication interface of the computer. For example, the CPU may read the related score calculation program from a program recording medium such as a program storage device of a computer, and operate as the collecting unit 52 using the communication interface according to the related score calculation program. Further, the word extraction unit 53 and the score calculation unit 54 are also realized by, for example, the CPU of the above-mentioned computer that operates according to the related score calculation program. That is, as described above, the CPU that has read the related score calculation program may operate as the word extraction unit 53 and the score calculation unit 54 according to the related score calculation program. The storage unit 5 is realized by the storage device of the above-mentioned computer. Further, the collection unit 52, the word extraction unit 53, and the score calculation unit 54 may be realized by separate hardware.

The fourth embodiment may be applied to a modification of the first embodiment (see FIG. 5), a second embodiment (see FIG. 7), and a third embodiment (see FIG. 14). That is, in the modified example of the first embodiment (see FIG. 5), the collection unit 2, the word extraction unit 3, and the score calculation unit 4 are divided into the collection unit 52, the word extraction unit 53, and the score described in the fourth embodiment. It may be replaced with the calculation unit 54. Further, in the second embodiment (see FIG. 7), the collection unit 2, the word extraction unit 3 and the score calculation unit 4 are combined with the collection unit 52, the word extraction unit 53 and the score calculation unit 54 described in the fourth embodiment. May be replaced with. Further, in the third embodiment (see FIG. 14), the collection unit 2, the word extraction unit 3 and the score calculation unit 4 are combined with the collection unit 52, the word extraction unit 53 and the score calculation unit 54 described in the fourth embodiment. May be replaced with.

FIG. 18 is a schematic block diagram showing a configuration example of a computer according to each embodiment of the present invention. The computer 1000 includes a CPU 1001, a main storage device 1002, an auxiliary storage device 1003, an interface 1004, and a communication interface 1005.

The related score calculation system 1 of each embodiment of the present invention is implemented in the computer 1000. The operation of the related score calculation system 1 is stored in the auxiliary storage device 1003 in the form of the related score calculation program. The CPU 1001 reads the related score calculation program from the auxiliary storage device 1003, deploys it to the main storage device 1002, and executes the above processing according to the related score calculation program.

Auxiliary storage 1003 is an example of a non-temporary tangible medium. Other examples of non-temporary tangible media include magnetic disks, optical magnetic disks, CD-ROMs (Compact Disk Read Only Memory), DVD-ROMs (Digital Versatile Disk Read Only Memory), which are connected via interface 1004. Examples include semiconductor memory. Further, when this program is distributed to the computer 1000 by a communication line, the distributed computer 1000 may expand the program to the main storage device 1002 and execute the above processing.

Further, the program may be for realizing a part of the above-mentioned processing. Further, the program may be a difference program that realizes the above-mentioned processing in combination with another program already stored in the auxiliary storage device 1003.

Further, a part or all of each component may be realized by a general-purpose or dedicated circuitry, a processor, or a combination thereof. These may be composed of a single chip or may be composed of a plurality of chips connected via a bus. A part or all of each component may be realized by the combination of the circuit or the like and the program described above.

When a part or all of each component is realized by a plurality of information processing devices and circuits, the plurality of information processing devices and circuits may be centrally arranged or distributed. For example, the information processing device, the circuit, and the like may be realized as a form in which each is connected via a communication network, such as a client-and-server system and a cloud computing system.

Next, the outline of the present invention will be described. FIG. 19 is a block diagram showing an outline of the present invention. The related score calculation system of the present invention includes a collecting unit 82, a word extraction unit 83, and a related score calculation unit 84.

The collection unit 82 (for example, the collection unit 2) collects an operation log, which is a record of the user operating the file, from the terminal device.

The word extraction unit 83 (for example, the word extraction unit 3) extracts words from the file names described in each operation log.

The related score calculation unit 84 (for example, the score calculation unit 4) calculates a related score indicating the strength of the relationship between the user and the word based on each operation log.

Such a structure can clarify the strength of the relationship between a person and a word in an organization.

FIG. 20 is a block diagram showing another example of the outline of the present invention. The related score calculation system of the present invention includes a collecting unit 86, a word extraction unit 87, and a related score calculation unit 88.

The collecting unit 86 (for example, the collecting unit 52) collects schedule information describing a person, an event name, and a time zone in which the person is involved in an event having the event name.

The word extraction unit 87 (for example, the word extraction unit 53) extracts words from the event names described in each schedule information.

The related score calculation unit 88 (for example, the score calculation unit 54) calculates a related score indicating the strength of the relationship between a person and a word based on each schedule information.

Such a structure can also clarify the strength of the relationship between a person and a word in an organization.

Each of the above embodiments of the present invention may be described as in the appendix below, but is not limited to the following.

(Appendix 1)
A collection unit that collects operation logs, which are records of user operations on files, from terminal devices.
A word extractor that extracts words from the file names described in each operation log,
A related score calculation system including a related score calculation unit that calculates a related score indicating the strength of the relationship between a user and a word based on each operation log.

(Appendix 2)
The related score calculation unit calculates the total operation time of the one user for each file containing the one word in the file name as the related score of one user and one word. The related score described in Appendix 1. Calculation system.

(Appendix 3)
The related score calculation unit calculates the total number of key touches when the one user operates each file containing the one word in the file name as the related score of one user and one word. Appendix 1 Related score calculation system described in.

(Appendix 4)
The related score calculation unit
The related score of one user and one word,
The ratio of the total operation time of the one user for each file containing the one word to the total operation time of all users in the organization for each file containing the one word in the file name. ,
The one user for each file containing the one word in the file name with respect to the sum of the sums when the sum of the operation times of the one user for each file containing the word is obtained for each word. The related score calculation system described in Appendix 1, which is calculated based on the ratio of the total operation time of.

(Appendix 5)
The keyword reception department that accepts search keywords and
It is equipped with a search unit that executes a search according to the search keyword.
The keyword reception department
Accept words as search keywords,
The search unit
The related score calculation system according to any one of Supplements 1 to 4, which searches for a user ID corresponding to the word based on the related score.

(Appendix 6)
The keyword reception department that accepts search keywords and
It is equipped with a search unit that executes a search according to the search keyword.
The keyword reception department
Accepts user ID as a search keyword,
The search unit
The related score calculation system according to any one of Supplements 1 to 5, which searches for a word corresponding to the user ID based on the related score.

(Appendix 7)
A first table generator that generates a first table that describes the relationship between a user ID, a word, and a related score.
A second table generator that describes the degree of relevance that represents the strength of the relevance between words calculated based on the first table, and a second table generator that generates the second table.
The keyword reception department that accepts search keywords and
It is equipped with a search unit that executes a search according to the search keyword.
The keyword reception department
Accept words as search keywords,
The search unit
The related score calculation system according to any one of Supplementary note 1 to Supplementary note 4, which searches for a user ID corresponding to the word based on the first table and the second table.

(Appendix 8)
A first table generator that generates a first table that describes the relationship between a user ID, a word, and a related score.
A second table generator that describes the degree of relevance that represents the strength of the relevance between words calculated based on the first table, and a second table generator that generates the second table.
The keyword reception department that accepts search keywords and
It is equipped with a search unit that executes a search according to the search keyword.
The keyword reception department
Accepts user ID as a search keyword,
The search unit
The related score calculation system according to any one of Supplementary note 1, Supplementary note 4, and Supplementary note 7, which searches for a word according to the user ID based on the first table and the second table.

(Appendix 9)
A clustering part that clusters words based on the second table,
A word presentation unit that presents words for each cluster,
It is equipped with a deletion target reception unit that accepts the designation of words to be excluded from the first table and the second table among the presented words.
The first table generator is
Exclude the word specified as the word to be excluded, generate the first table again, and
The second table generator is
The related score calculation system according to Appendix 7 or Appendix 8, which regenerates the second table based on the first table.

(Appendix 10)
A collection unit that collects schedule information that describes a person, an event name, and a time zone in which the person was involved in an event having the event name.
A word extractor that extracts words from the event names described in each schedule information,
A related score calculation system including a related score calculation unit that calculates a related score indicating the strength of the relationship between a person and a word based on each schedule information.

(Appendix 11)
The operation log, which is a record of the user operating the file, is collected from the terminal device and
Extract words from the file names described in each operation log and
A related score calculation method characterized in that a related score indicating the strength of the relationship between a user and a word is calculated based on each operation log.

(Appendix 12)
Collect schedule information that describes the person, the event name, and the time zone in which the person was involved in the event having the event name.
Extract words from the event names described in each schedule information,
A related score calculation method characterized in that a related score indicating the strength of a person-word relationship is calculated based on each schedule information.

(Appendix 13)
On the computer
Collection processing that collects operation logs, which are records of user operations on files, from terminal devices.
Word extraction processing that extracts words from the file names described in each operation log, and
A related score calculation program for executing a related score calculation process that calculates a related score indicating the strength of the relationship between a user and a word based on each operation log.

(Appendix 14)
On the computer
A collection process that collects schedule information that describes a person, an event name, and a time zone in which the person was involved in an event having the event name.
Word extraction process that extracts words from the event name described in each schedule information, and
A related score calculation program for executing a related score calculation process that calculates a related score that indicates the strength of the relationship between a person and a word based on each schedule information.

The present invention is suitably applied to a related score calculation system that quantifies the strength of a relationship between a person and a word.

1 Related score calculation system 2,52 Collection unit 3,53 Word extraction unit 4,54 Score calculation unit 5 Storage unit 6 Keyword reception unit 7,17 Search unit 8 Output unit 31 Clustering unit 32 Cluster output unit 33 Excluded word reception unit

Claims (3)

A collection unit that collects schedule information that describes a person, an event name, and a time zone in which the person was involved in an event having the event name.
A word extractor that extracts words from the event names described in each schedule information,
It is equipped with a related score calculation unit that calculates a related score that indicates the strength of the relationship between a person and a word based on each schedule information.
The related score calculation unit
The related score of one person and one word,
The total event participation time of the one person for each event containing the one word in the event name with respect to the total event participation time of all persons in the organization for each event containing the one word in the event name. Percentage and
The above-mentioned one for each event containing the one word in the event name with respect to the total of the total of the sum of the event participation times of the one person for each event including the word in the event name. A related score calculation system characterized in that it is calculated based on the ratio of the total time of a person's participation in an event. The computer
Collect schedule information that describes the person, the event name, and the time zone in which the person was involved in the event having the event name.
Extract words from the event names described in each schedule information,
Based on each schedule information, a relation score indicating the strength of the relation between a person and a word is calculated.
When calculating the relevant score,
The related score of one person and one word,
The total event participation time of the one person for each event containing the one word in the event name with respect to the total event participation time of all persons in the organization for each event containing the one word in the event name. Percentage and
The above-mentioned one for each event containing the one word in the event name with respect to the total of the total of the sum of the event participation times of the one person for each event including the word in the event name. A related score calculation method characterized in that it is calculated based on the ratio of the total time of a person's participation in an event. On the computer
A collection process that collects schedule information that describes a person, an event name, and a time zone in which the person was involved in an event having the event name.
Word extraction process that extracts words from the event name described in each schedule information, and
Based on each schedule information, the related score calculation process that calculates the related score indicating the strength of the relationship between the person and the word is executed.
In the related score calculation process,
The related score of one person and one word,
The total event participation time of the one person for each event containing the one word in the event name with respect to the total event participation time of all persons in the organization for each event containing the one word in the event name. Percentage and
The above-mentioned one for each event containing the one word in the event name with respect to the total of the total of the sum of the event participation times of the one person for each event including the word in the event name. A related score calculation program for calculating based on the ratio of the total time a person participates in an event.

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