JP2024077184A - Cohesion Estimator - Google Patents

Abstract

[Problem] The present invention aims to provide a cohesion determination device that determines whether a user is a human resource capable of improving the cohesion (degree of concentration) of communication in a team in which the user is about to join. [Solution] In a cohesion estimation device 400, a cohesion estimation unit 406 estimates whether a user will improve the cohesion of a team based on a user vector of the user generated based on log data of communication between users. With this configuration, cohesion can be determined from the log data without looking at the text log (content) of the communication, and privacy and confidential business information can be protected. [Selected Figure] Figure 10

Description

The present invention relates to a cohesion estimation device that judges a user's cohesion.

When communicating using Slack (registered trademark) and Teams (registered trademark), it is difficult to determine from online communication alone what type of talent will energize a team (group).

Patent document 1 describes the objective evaluation of communication in situations where online communication is the norm, such as meetings and lectures, in order to make such communication more efficient.

Patent Publication No. 7100938

However, the technology described in Patent Document 1 does not allow for a determination of whether a participating user is a talent that will improve the team's cohesion.

The present invention aims to provide a cohesion estimation device that determines whether a user is a person who can improve the cohesion (concentration) of communication in a team in which the user is about to join.

The cohesion estimation device of the present invention includes an estimation unit that estimates whether a user improves the cohesion of a team based on a user vector of the user generated based on log data of communication between users.

The present invention makes it possible to determine whether a user is a person who can improve cohesion in the team to which he or she belongs.

1 is a diagram showing a system configuration including a user vector generating device 100 according to the present disclosure. FIG. 2 is a diagram illustrating a functional configuration of a user vector generating device 100 according to the present disclosure. 2 is a diagram illustrating a functional configuration of a user vector generation unit 102 according to the present disclosure. 11 is a diagram showing log data of chat data stored in chat server 200. FIG. 4 is a flowchart showing an operation of the user vector generating device 100. FIG. 13 is a diagram illustrating an example of creating an adjacency matrix. FIG. 13 is a diagram showing a specific example of how to create an adjacency matrix. FIG. 13 is a diagram illustrating generation of sequence data from an adjacency matrix. FIG. 13 is a schematic diagram showing when a new user 1 joins a talk room (a certain channel). FIG. 4 is a diagram showing a functional configuration of the cohesion estimation device 400. 13 is a flowchart showing the learning operations of the user vector generating device 100 and the cohesion estimation device 400. FIG. 11 is an explanatory diagram specifically showing a learning process. FIG. 13 is a diagram showing user vectors x and cohesion a generated from log data. 13 is a flowchart showing an estimation process using the LightGBM model 405 in the cohesion estimation device 400. FIG. 2 is a diagram illustrating a cohesion estimation process in the cohesion estimation device 400. FIG. 1 is a diagram illustrating an example of a hardware configuration of a user vector generating device 100 and a cohesion estimation device 400 according to an embodiment of the present disclosure.

Embodiments of the present disclosure will be described with reference to the accompanying drawings. Where possible, identical parts will be designated by the same reference numerals, and duplicate explanations will be omitted.

Figure 1 is a diagram showing a system configuration including a user vector generation device 100 of the present disclosure. As shown in the figure, the user vector generation device 100 of the present disclosure generates a user vector from chat data in a chat server 200.

The chat server 200 is a control server that manages the sending and receiving of chat messages from multiple user terminals 300. It creates a talk room for each channel set by the user terminal 300, and manages chat messages in that talk room.

The user terminal 300 can communicate with other users by sending and receiving chat messages via the chat server 200.

The cohesion estimation device 400 estimates the cohesion of a user using the user vector generated by the user vector generation device 100. Cohesion in this disclosure indicates how dense (team activity) communication is in a team (group) in which a user is involved. In other words, cohesion is the degree of concentration of communication, and improving cohesion is intended to mean that the degree of concentration of communication is improved. In that sense, improving cohesion may also mean that communication is activated.

Figure 2 is a diagram showing the functional configuration of the user vector generation device 100 of the present disclosure. As shown in the figure, the user vector generation device 100 includes a log storage unit 101, a user vector generation unit 102, and a user vector storage unit 103. Figure 3 is a diagram showing a more detailed functional configuration of the user vector generation unit 102. The user vector generation unit 102 includes an adjacency matrix generation unit 102a, a probability matrix generation unit 102b, a sequence data generation unit 102c, and a SKIPGRAM model 102d.

The log storage unit 101 is a part that stores log data of chat data stored in the chat server 200. FIG. 4 shows a specific example. As shown in the figure, the talk room channel ID, message sending time, message sender ID, and message recipient ID are stored in association with each other.

The user vector generation unit 102 is a part that generates user vectors from log data. As shown in FIG. 3, the user vector generation unit 102 includes an adjacency matrix generation unit 102a, a probability matrix generation unit 102b, a sequence data generation unit 102c, and a SKIPGRAM model 102d. Details of this generation process will be described later.

The user vector storage unit 103 is a part that stores user vectors. These user vectors can be used for cohesion estimation, etc., as described below.

Next, the operation of the user vector generation device 100 will be described. FIG. 5 is a flowchart showing the operation. The user vector generation unit 102 acquires log data from the log storage unit 101 (S101). The user vector generation unit 102 (adjacency matrix generation unit 102a) generates an adjacency matrix of a social graph from log data indicating a chat communication log (S102). The user vector generation unit 102 (probability matrix generation unit 102b) obtains a probability matrix from the adjacency matrix (S103). The user vector generation unit 102 (sequence data generation unit 102c) generates sequence data by the RandomWalk algorithm from the probability according to the probability matrix (S104). The user vector generation unit 102 (SKIPGRAM model 102d) vectorizes the sequence data (S105). This vectorized sequence data becomes a user vector.

Now, each step in the above operation will be described in detail. FIG. 6 is a schematic diagram of the user vector generation process in process S102. FIG. 6 is an explanatory diagram when the adjacency matrix generation unit 102a creates an adjacency matrix from chat messages. In this disclosure, the adjacency matrix Aij is created from chat messages C. This adjacency matrix Aij is composed of elements aij, where aij indicates, for example, the number of times a message was sent from user i to user j. In other words, it is a matrix that indicates the frequency of sending and receiving chat messages between users.

In the present disclosure, an adjacency matrix is created for all users of all channels. An adjacency matrix may be created for users for each channel, or for users for each channel genre. A genre here refers to, for example, work-related channels, employee benefits-related channels, etc. Since this is Slack, chat message exchanges may not be limited to work-related.

Figure 7 shows a more specific example of how to create an adjacency matrix. As shown in Figure 7(a), chat message C is stored as log data in the log storage unit 101. Then, as shown in Figure 7(b), the message direction between users is extracted as a data string. Here, UserA:UserB indicates that UserA sent a message to UserB. The same is true for the others. Figure 7(c) shows the adjacency matrix. Since there is not generally such a thing as UserA:UserA, the diagonal element is 0, and the other elements indicate the frequency of that message direction.

Figure 8 shows how sequence data is generated from an adjacency matrix by the sequence data generation unit 102c. This sequence data is based on a probability matrix obtained from the adjacency matrix, and is data extracted by RandomWalk. RandomWalk is an algorithm in which the next position to appear is determined probabilistically at random, and is a well-known algorithm that can obtain the latent characteristics of a graph.

Figure 8 (a) is a schematic diagram of chat messages between users, which includes log data. As described above, the log data indicates the frequency of sending and receiving chat messages between users. When sequence data is generated probabilistically according to the transition probability (ratio of edge weights), sequence data such as that shown in Figure 8 (b) is generated.

Figure 8 (b) shows sequence data, which was obtained by RandomWalk. Here, one piece of sequence data contains three nodes. For example, sequence 1, which is sequence data, shows that User A → User B → User C are connected in that order. This shows the graphical characteristics of the users.

Figure 8 (c) is a diagram showing adjacency matrix A. This adjacency matrix A is obtained according to the process described in Figure 7 above. For example, it shows that the frequency of sending chat messages from user A to user B is 11 times. Based on this frequency, the probability matrix generation unit 102b generates the probability matrix shown in Figure 8 (d). This shows a matrix of the probability of transitioning from user i to user j. Note that in this disclosure, transition refers to the probability that a user will send a chat message.

This probability matrix is calculated based on (weight from user i to user j)/(sum of weights going out of user i).

Using the probability matrix generated in this way, sequence data is generated by RandomWalk. As shown in the figure, sequence data characterizes the overall connections between users.

By inputting this sequence data, the user vector generation unit 102 (SKIPGRAM model 102d) can output user vectors obtained by vectorizing users. The SKIPGRAM model 102d is a neural network with one intermediate layer (hidden layer). This SKIPGRAM model 102d is often used as a model for learning natural languages, and is a model that learns "the probability that other words (in the vocabulary) are in the vicinity of a certain word when the input is given." In the present disclosure, this SKIPGRAM model 102d is used to learn the probability of users who are in the vicinity (who communicate frequently) when a certain user is given, thereby learning user vectors based on communication. In the present disclosure, a user vector for each user is obtained from the intermediate layer in the SKIPGRAM model 102d learned in this way.

Next, the cohesion of the present disclosure will be described. FIG. 9 is a schematic diagram showing when user 1 newly joins a talk room (a certain channel) configured to enable communication between user 2, user 3, and user 4. FIG. 9(a) shows the time when user 1 attempts to join the talk room at time t. FIG. 9(b) shows subsequent chat communication between user 1 and user 4.

As shown in FIG. 9(b), the addition of user 1 may improve cohesion (degree of concentration of communication) among members including users 2-4. In this disclosure, the objective is to estimate users who improve cohesion for a certain team.

Figure 10 is a diagram showing the functional configuration of the cohesion estimation device 400. As shown in the figure, the cohesion estimation device 400 includes a sample mean vector calculation unit 401, a cohesion calculation unit 402, an input vector generation unit 403, a learning unit 404, a LightGBM model 405, and a cohesion estimation unit 406.

The sample average vector calculation unit 401 is a part that calculates the sample average user vector, which is the average value of the user vectors of users (e.g., users 2-4) before the target user (e.g., user 1) joined the talk room. This sample average user vector is based on the user vector x i calculated based on the communication history (log data) for a certain period of time in the past (e.g., t-Δt).

凝集性算出部４０２は、以下の数式に基づいてチームｃにおける凝集性ａを算出する部分である。チームとは、ユーザが参加しているトークルームを示す。

The cohesion calculation unit 402 is a part that calculates the cohesion a in a team c based on the following formula: A team refers to a talk room in which a user participates.

In the above formula, the weight w _ij is a numerical value determined based on log data, for example, based on communication frequency. Every time a message is sent from user i to user j, 1 is added to the weight w _ij .

In the above equation, the numerator represents the density within the team, and the denominator represents the density of communication with people outside the team. In other words, cohesion is the ratio of the density of communication with people outside the team to the density of communication with people within the team. The greater the breadth and intensity of communication with people within the team compared to the breadth and intensity of communication with people outside the team, the higher the cohesion.

The input vector generation unit 403 is a part that generates an input vector based on the generated sample mean vector and the user vector of the user who is the subject of the cohesion measurement. This input vector is composed of the sample mean vector and the user vector concatenated.

For example, assume that a user vector x ₁ and a sample mean vector are calculated as follows:

入力用ベクトル生成部４０３は、以下の通り、ユーザベクトルｘ_１および標本平均ベクトルを、縦に並べて連結することで、入力用ベクトルを生成する。

The input vector generation unit 403 generates an input vector by vertically arranging and concatenating the user vector _x1 and the sample mean vector as follows.

学習部４０４は、入力用ベクトルを説明変数とし、ユーザの加入前後に算出された凝集性の差に基づいた情報を目的変数として、公知の機械学習によりLightＧＢＭモデル４０５を学習して生成する部分である。

The learning unit 404 is a part that learns and generates a LightGBM model 405 using well-known machine learning, using the input vector as an explanatory variable and information based on the difference in cohesion calculated before and after the user's subscription as the objective variable.

The LightGBM model 405 is an estimation model that, when a user vector is input, outputs whether or not cohesion is improved.

The cohesion estimation unit 406 inputs the user vector into the LightGBM model 405, receives the output information on whether or not the cohesion of the user will be improved, and makes an estimation.

Next, the operation of the user vector generation device 100 and the cohesion estimation device 400 will be described. FIG. 11 is a flowchart showing the learning operation of the user vector generation device 100 and the cohesion estimation device 400.

The user vector generating device 100 generates a user vector and stores it in the user vector storage unit 103 (S201).

The sample mean vector calculation unit 401 calculates the sample mean vector of the target team in a predetermined period (first period) (S202). The input vector generation unit 403 generates an input vector (S203).

The cohesion calculation unit 402 calculates the cohesion of the target team for a predetermined period (first period) that is the same as the above (S204). The cohesion calculation unit 402 also calculates the cohesion of the target team for another predetermined period (second period) after the above-mentioned predetermined period has elapsed (S205).

The learning unit 404 is a part that learns the LightGBM model 405 from the input vector and sample mean vector, and information based on the cohesion in the above-mentioned specified period and a subsequent specified period. The details are as follows. The learning unit 404 subtracts the cohesion in the subsequent period (second period) from the cohesion in the specified period (first period), and outputs 1 or 0 based on whether the value is positive or negative (S206). The learning unit 404 then uses the input vector as an explanatory variable and learns information based on the difference in cohesion at that time as a target variable (S207).

Fig. 12 is an explanatory diagram specifically showing the above learning process, and Fig. 13 is a diagram showing user vector x and cohesion a generated from log data. As shown in Fig. 12(a), the user vector generation device 100 generates user vectors _x2 , _x3 , and _x4 for users 2 to 4 based on the log data at time t-Δt (S201).

For example, in the case where there are teams a to c as shown in Fig. 13, a user vector _x1 is generated from the log data of all the teams (teams a to c). As described above, a team refers to a talk room (or a channel) in a chat application.

As shown in the figure, the sample average vector calculation unit 401 calculates a sample average vector from these user vectors (S202). As shown in the figure, this sample average vector is the sum of all user vectors divided by the number of users. As shown in Figure 13, the sample average vector is calculated from the log data of team C.

As shown in FIG. 12(b), the cohesion calculation unit 402 calculates the cohesion a according to the above-mentioned formula (1).

As shown in FIG. 12(c), the user vector of user 1, who is the subject of cohesion measurement, is generated by the user vector generation device 100 (S201). This user vector is for the time t-Δt in the log data.

12(d), an input vector is generated from the user vector _x1 of user 1 and the user vectors (sample average vectors) of users 2-4 by the input vector generation unit 403 (S203). In this input vector, the user vector _x1 is a vector based on all teams, and the sample average vector is a team vector based on team C.

As shown in FIG. 12(e), the cohesion calculation unit 402 calculates the cohesion of each user vector in one team C for a predetermined period (first period, second period) (S204, S205). Then, the learning unit 404 calculates the difference (or comparison result) of the cohesion and calculates its sign (S206).

13, cohesion a _c [t- ^{Δt, t]} is calculated from the log data of team c in the first period "t-Δt", while cohesion a _c ^{[t, t+Δt]} is calculated from the log data of team c in the second period "t+Δt".

If a _c ^{[t, t + Δt]} - _{a c} ^{[t - Δt, t]} is a positive value, a binary label is created that is 1, and if it is negative, a binary label is created that is 0. That is, in this binary label, 1 indicates that cohesion has improved, and 0 indicates that cohesion has decreased.

The LightGBM model 405 is trained using these input vectors as explanatory variables and the binary labels as objective variables. The input vectors consist of the user vector of the user to be measured and the sample mean vector of the team to which the user belongs. The sample mean vector indicates the team vector of that team. Therefore, the input vector indicates the relationship between the user vector to be measured and the team vector.

Figure 14 is a flowchart showing the estimation process using the LightGBM model 405 in the cohesion estimation device 400. The cohesion estimation unit 406 acquires the user vector generated in the user vector generation device 100 (S301). The cohesion estimation unit 406 inputs the user vector to the LightGBM model 405 (S302), acquires the output, and performs cohesion estimation (S303).

Figure 15 is a diagram showing a schematic diagram of the cohesion estimation process in the cohesion estimation device 400. As shown in Figure 15 (a), in the user vector generation process, graph data is generated from chat message log data, and a user vector is generated. After that, the user vector is input to the trained LightGBM model, and the cohesion is output. In the figure, the cohesion is output as 0 or 1.

Figure 15(b) is a diagram showing this process with more specific values. As shown in the figure, the user vector is composed of 256-dimensional numerical parameters. This user vector is input to the LightGBM model 405, and its value is output. For example, in Figure 15(b), the cohesion of user A is output as 0, and user A can be determined to be a person who does not contribute to improving cohesion.

Next, the effects of the user vector generation device 100 of the present disclosure will be described. In the user vector generation device 100, the log storage unit 101 stores log data indicating communication between users. The user vector generation unit 102 generates a user vector based on the log data.

This log data consists of the source and destination of communications and is stored for each user.

With this configuration, a user vector representing the characteristics of a user can be generated from the log data. In particular, since the user vector is generated based on which user communicated with which other user, the confidentiality of the content of the communication can be maintained.

In addition, in the present disclosure, the user vector generation unit 102 generates an adjacency matrix representing a social graph based on the log data, and generates a probability matrix based on the communication frequency of the log data from the adjacency matrix. Then, the user vector generation unit 102 generates sequence data indicating connections between users based on the probability matrix, and generates a user vector based on the sequence data.

With this configuration, user vectors can be generated from log data on who spoke (communicated) with whom and how many times, without using the contents of the actual text logs of the communications. This reduces concerns about privacy or confidential business information.

The user vector generation unit 102 can generate sequence data indicating connections between users from the log data without using an adjacency matrix or the like, and generate a user vector based on the sequence data. The sequence data is generated based on a communication probability based on the communication frequency in the log data.

In the present disclosure, the log data includes communication in multiple teams, and the user vector generation unit 102 generates a user vector based on the log data of communication in all or part of the multiple teams. Here, a team (group) refers to a channel used in a chat app such as Teams (registered trademark) or Slack (registered trademark), and may be replaced with a channel.

In this disclosure, communication is based on at least one of email, chat, or SNS. As described above, chat is chat via Teams or Slack. Email can also be applied because the sender and destination can be defined.

In addition, this can also be applied to SNS (Social Network Services) as long as the frequency of communication with each contact can be identified.

It can also be applied to telephone calls, calls using SNS, etc. In such cases, the duration of the call may also be taken into consideration. The number of text characters may also be taken into consideration in chats, etc. For example, communications with short call durations or short number of characters may not be used in generating user vectors.

Next, the effect of the cohesion estimation device 400 of the present disclosure will be described. In the cohesion estimation device 400 of the present disclosure, the cohesion estimation unit 406 estimates whether a user will improve the cohesion of a team based on a user vector of the user generated based on log data of communication between users.

This configuration makes it possible to determine cohesion from log data without looking at the text log (content) of the communication, thereby protecting privacy and confidential business information.

The cohesion estimation unit 406 further adds the sample mean vector (team vector) of a team to which a user is to join, and estimates whether the user will improve cohesion in the team.

The cohesion estimation device 400 of the present disclosure further includes a LightGBM model 405 (estimation model) that inputs a user vector and a sample mean vector (team vector) of a single user and estimates whether the single user will improve cohesion in a single team. The cohesion estimation unit 406 then uses the LightGBM model 405 to estimate whether the single user will improve cohesion in a single team.

This LightGBM model 405 is trained using the learning user vectors prepared as learning data as explanatory variables and the cohesion improvement result data for a team prepared as learning data as objective variables.

This cohesion improvement result data is based on the cohesion a of the team before and after the user being measured joins the team (see formula (1) above), specifically, it is obtained by comparing the cohesion a _c ^{[t-Δt, t]} for a specified period (first period) before the user joins the team with the cohesion a _c ^{[t, t+Δt]} for a specified period (second period) after the user joins the team.

If team cohesion improves after a user joins, the user can be determined to be a valuable asset that will contribute to improving cohesion.

The learning user vector is constructed based on the user vector of the user to be learned and the sample mean vector (team vector) of the team the user was previously attempting to join. Here, the user vector is generated based on all log data, and the sample mean vector (team vector) is generated based on the log data of the team.

In this disclosure, cohesion is calculated based on the density of communication within the team and the density of communication with outside the team. This cohesion is an indicator that the addition of a user increases the concentration of communication within the team.

In this disclosure, user vectors are generated based on the transmission destinations and number of transmissions in the log data.

The cohesion estimation device disclosed herein has the following configuration:

[1]
an estimation unit that estimates whether a user improves team cohesion based on a user vector of the user generated based on log data of communication between users;
The cohesion estimation device comprises:

[2]
The estimation unit estimates whether the one user will improve cohesion in the one team by further adding a team vector in the one team to which the one user will join;
The cohesion estimation device according to [1],

[3]
The method further includes an estimation model that receives a user vector of the one user and the team vector and estimates whether the one user will improve cohesion in the one team;
The estimation unit estimates whether the one user improves cohesion in the one team by using the estimation model.
The cohesion estimation device according to [2].

[4]
The estimation model is
The learning user vector prepared as the learning data is used as an explanatory variable, and the cohesion improvement result data in one team prepared as the learning data is used as a target variable.
The cohesion estimation device according to [3].

[5]
The training user vector is
Based on the user vector of the user to be learned and the team vector of the team to which the user is to join,
The cohesion estimation device according to [4].

[6]
The user vector is generated based on all log data;
The team vector is generated based on log data of the team.
The cohesion estimation device according to [5].

[7]
The cohesiveness improvement result data is
Based on the cohesion of the team before and after the user to be measured joins the team,
The cohesion estimation device according to [5] or [6].

[8]
The cohesiveness improvement result data is
by comparing the cohesion of the user for a predetermined period before joining the team with the cohesion of the user for a predetermined period after joining the team;
The cohesion estimation device according to [7].

[9]
The cohesion is calculated based on a density of communication within the team and a density of communication with outside the team;
The cohesion is an index showing that the intensiveness of communication within the team is improved by the addition of the one user.
The cohesion estimation device according to any one of [1] to [8] [10]
The user vector is
Generated based on the transmission destination and the number of transmissions in the log data.
The cohesion estimation device according to any one of [1] to [9].

The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one device that is physically or logically coupled, or may be realized using two or more devices that are physically or logically separated and connected directly or indirectly (e.g., using wires, wirelessly, etc.) and these multiple devices. The functional blocks may be realized by combining the one device or the multiple devices with software.

Functions include, but are not limited to, judgement, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, regarding, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs the transmission function is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on the method of realization for either of these.

For example, the user vector generation device 100 and the cohesion estimation device 400 in one embodiment of the present disclosure may function as a computer that performs processing of the user vector generation method or the cohesion estimation method of the present disclosure. FIG. 16 is a diagram showing an example of the hardware configuration of the user vector generation device 100 and the cohesion estimation device 400 according to one embodiment of the present disclosure. The above-mentioned user vector generation device 100 and the cohesion estimation device 400 may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc.

In the following description, the term "apparatus" can be interpreted as a circuit, device, unit, etc. The hardware configuration of the user vector generation apparatus 100 and the cohesion estimation apparatus 400 may be configured to include one or more of the apparatuses shown in the figure, or may be configured to exclude some of the apparatuses.

The functions of the user vector generating device 100 and the cohesion estimating device 400 are realized by loading specific software (programs) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations, control communications via the communication device 1004, and control at least one of the reading and writing of data in the memory 1002 and storage 1003.

The processor 1001, for example, operates an operating system to control the entire computer. The processor 1001 may be configured with a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, the above-mentioned user vector generation unit 102, learning unit 404, and cohesion estimation unit 406, etc. may be realized by the processor 1001.

The processor 1001 also reads out programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes according to these. As the programs, those that cause a computer to execute at least a part of the operations described in the above-mentioned embodiment are used. For example, the user vector generation unit 102, the learning unit 404, and the cohesion estimation unit 406 may be realized by a control program stored in the memory 1002 and operating in the processor 1001, and other functional blocks may be similarly realized. Although the above-mentioned various processes have been described as being executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The programs may be transmitted from a network via a telecommunication line.

The memory 1002 is a computer-readable recording medium, and may be composed of at least one of, for example, a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), etc. The memory 1002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 1002 can store a program (program code), software module, etc. that is executable to implement a user vector generation method or a cohesion estimation method according to one embodiment of the present disclosure.

Storage 1003 is a computer-readable recording medium, and may be, for example, at least one of an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, and the like. Storage 1003 may also be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other suitable medium including at least one of memory 1002 and storage 1003.

The communication device 1004 is hardware (transmitting/receiving device) for communicating between computers via at least one of a wired network and a wireless network, and is also called, for example, a network device, a network controller, a network card, a communication module, etc. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc., to realize, for example, at least one of Frequency Division Duplex (FDD) and Time Division Duplex (TDD).

The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that performs output to the outside. Note that the input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

In addition, each device such as the processor 1001 and memory 1002 is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

The user vector generating device 100 and the cohesion estimating device 400 may also be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

The notification of information is not limited to the aspects/embodiments described in the present disclosure, and may be performed using other methods. For example, the notification of information may be performed by physical layer signaling (e.g., Downlink Control Information (DCI), Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information (Master Information Block (MIB), System Information Block (SIB))), other signals, or a combination of these. In addition, the RRC signaling may be called an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, etc.

The processing steps, sequences, flow charts, etc. of each aspect/embodiment described in this disclosure may be reordered unless inconsistent. For example, the methods described in this disclosure present elements of various steps using an example order and are not limited to the particular order presented.

The input and output information may be stored in a specific location (e.g., memory) or may be managed using a management table. The input and output information may be overwritten, updated, or added to. The output information may be deleted. The input information may be transmitted to another device.

The determination may be based on a value represented by one bit (0 or 1), a Boolean (true or false) value, or a numerical comparison (e.g., with a predetermined value).

Each aspect/embodiment described in this disclosure may be used alone, in combination, or switched depending on the execution. In addition, notification of specific information (e.g., notification that "X is the case") is not limited to being done explicitly, but may be done implicitly (e.g., not notifying the specific information).

Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended as an illustrative example and does not have any limiting meaning on the present disclosure.

Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

Software, instructions, information, etc. may also be transmitted and received via a transmission medium. For example, if the software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and/or wireless technologies (such as infrared, microwave), then these wired and/or wireless technologies are included within the definition of a transmission medium.

The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

Note that the terms described in this disclosure and the terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of the channel and the symbol may be a signal (signaling). Also, the signal may be a message. Also, a component carrier (CC) may be called a carrier frequency, a cell, a frequency carrier, etc.

In addition, the information, parameters, etc. described in this disclosure may be represented using absolute values, may be represented using relative values from a predetermined value, or may be represented using other corresponding information. For example, radio resources may be indicated by an index.

The names used for the parameters described above are not intended to be limiting in any way. Furthermore, the formulas etc. using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (e.g., PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not intended to be limiting in any way.

In this disclosure, terms such as "Mobile Station (MS)," "user terminal," "User Equipment (UE)," and "terminal" may be used interchangeably.

A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable terminology.

As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and the like. Additionally, "judgment" and "decision" can include considering resolving, selecting, choosing, establishing, comparing, etc., to have been "judged" or "decided." In other words, "judgment" and "decision" can include considering some action to have been "judged" or "decided." Additionally, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

The terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access." As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using at least one of one or more wires, cables, and printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

Any reference to an element using a designation such as "first," "second," etc., used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must precede the second element in some way.

When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Additionally, the term "or," as used in this disclosure, is not intended to be an exclusive or.

In this disclosure, where articles have been added through translation, such as a, an, and the in English, the disclosure may include that the noun following these articles is in the plural.

In this disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "combined" may also be interpreted in the same way as "different."

100...user vector generation device, 200...chat server, 300...user terminal, 400...cohesion estimation device, 101...log storage unit, 102...user vector generation unit, 103...user vector storage unit, 401...sample average vector calculation unit, 402...cohesion calculation unit, 403...input vector generation unit, 404...learning unit, 405...LightGBM model, 406...cohesion estimation unit.

Claims (10)

an estimation unit that estimates whether a user improves team cohesion based on a user vector of the user generated based on log data of communication between users;
The cohesion estimation device comprises: The estimation unit estimates whether the one user will improve cohesion in the one team by further adding a team vector in the one team to which the one user will join;
The cohesion estimation device according to claim 1 , comprising: The method further includes an estimation model that receives a user vector of the one user and the team vector and estimates whether the one user will improve cohesion in the one team;
The estimation unit estimates whether the one user improves cohesion in the one team by using the estimation model.
The cohesion estimation device according to claim 2 . The estimation model is
The learning user vector prepared as the learning data is used as an explanatory variable, and the cohesion improvement result data in one team prepared as the learning data is used as a target variable.
The cohesion estimation device according to claim 3 . The training user vector is
Based on the user vector of the user to be learned and the team vector of the team to which the user is to join,
The cohesion estimation device according to claim 4 . The user vector is generated based on all log data;
The team vector is generated based on log data of the team.
The cohesion estimation device according to claim 5 . The cohesiveness improvement result data is
Based on the cohesion of the team before and after the user to be studied joins the team,
The cohesion estimation device according to claim 5 . The cohesiveness improvement result data is
by comparing the cohesion of the user for a predetermined period before joining the team with the cohesion of the user for a predetermined period after joining the team;
The cohesion estimation device according to claim 7 . The cohesion is calculated based on a density of communication within the team and a density of communication with outside the team;
The cohesion is an index showing that the intensiveness of communication within the team is improved by the addition of the one user.
The cohesion estimation device according to claim 1 . The user vector is
Generated based on the transmission destination and the number of transmissions in the log data.
The cohesion estimation device according to claim 1 .

Images