Group generation device, group generation method, and program

Abstract

This group generating device (2000) acquires personal information (10). The personal information (10) represents information pertaining to a plurality of candidate persons. The group generating device (2000) determines the respective allocation of the candidate persons into one or more groups on the basis of a mental state indicator pertaining to the mental state of each candidate person and a generation rule that is a rule for group composition based on the mental state indicators.

Description

[Technical Field]

[0001] The present disclosure relates to a group generation device, a group generation method, and a non-transitory computer-readable medium. [Background technology]

[0002] Techniques have been developed for automatically determining the allocation of people to groups. For example, Patent Document 1 discloses a system for automatically determining members of a project group so as to satisfy required specifications. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2007-042059 [Non-patent literature]

[0004] [Non-Patent Document 1] "Recommended Happiness Surveys (SWLS, Four Factors of Happiness, etc.)," ​​[online], Keio University Graduate School of System Design and Management, Human Systems Design Laboratory, [Retrieved February 16, 2022], Internet<URL: http: / / lab.sdm.keio.ac.jp / maenolab / questionnaire.html> Summary of the Invention [Problem to be solved by the invention]

[0005] The requirements disclosed in Patent Document 1 are programming ability, past experience, and the amount of time available to devote to the project. Therefore, Patent Document 1 does not disclose a method for determining the allocation of people to groups using indicators other than these. The present disclosure has been made in light of such issues, and one of its purposes is to provide a new technology for allocating people to groups. [Means for solving the problem]

[0006] The group generation device of the present disclosure has an acquisition means for acquiring personal information regarding a plurality of candidate persons, and a determination means for determining the allocation of the candidate persons to each of one or more groups based on a psychological state index value regarding the psychological state of each of the candidate persons and a group composition rule based on the psychological state index value.

[0007] The group generation method of the present disclosure is executed by a computer and includes an acquisition step of acquiring personal information about a plurality of candidate persons, and a determination step of determining allocation of the candidate persons to one or more groups based on a psychological state index value related to the psychological state of each candidate person and a group formation rule based on the psychological state index value.

[0008] The non-transitory computer-readable medium of the present disclosure stores a program that causes a computer to execute the information providing method of the present disclosure. [Effects of the Invention]

[0009] According to the present disclosure, a new technique for assigning people to groups is provided. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating an example of an outline of an operation of a group generation device according to an embodiment. [Figure 2] FIG. 2 is a block diagram illustrating a functional configuration of a group generation device. [Figure 3] FIG. 2 is a block diagram illustrating an example of a hardware configuration of a computer that realizes the group generation device. [Figure 4] 10 is a flowchart illustrating a flow of a process executed by the group generation device. [Figure 5] 10 is a flowchart showing the procedure for allocating candidates in accordance with example 5 of the generation rule. [Figure 6] FIG. 10 is a diagram illustrating an example of an operation screen provided by the group generation device. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In each drawing, the same or corresponding elements are designated by the same reference numerals, and duplicate explanations will be omitted as necessary for clarity. Furthermore, unless otherwise specified, predetermined values ​​such as predetermined values ​​and threshold values ​​are stored in advance in a storage device accessible from a device that uses the values. Furthermore, unless otherwise specified, the storage unit is composed of one or any number of storage devices.

[0012] <Summary> Fig. 1 is a diagram illustrating an overview of a group generation device 2000 according to an embodiment. Note that Fig. 1 is a diagram for facilitating understanding of the overview of the group generation device 2000, and the operation of the group generation device 2000 is not limited to that shown in Fig. 1.

[0013] The group generation device 2000 determines the person to be assigned to each of the multiple groups. Hereinafter, the candidates for the person to be assigned to the group will be referred to as "candidate person."

[0014] For example, the group generation device 2000 can be used to divide multiple people belonging to one organization into multiple groups. For example, the group generation device 2000 can be used to assign each of 200 employees to one of five departments. In this case, each employee belonging to the company becomes a candidate.

[0015] In addition, for example, the group generation device 2000 can be used to extract a predetermined number of people from multiple people belonging to an organization and create one or more new groups. For example, the group generation device 2000 can be used to form a team by selecting 10 employees from 50 candidate employees to form a new project team. In this case, each of the 50 candidate employees is a candidate. The group generation device 2000 generates a project team by selecting 10 appropriate people from the 50 candidate employees.

[0016] Note that when generating a group by selecting some people from a plurality of people in this way, the number of groups generated is not limited to one. For example, suppose you want to organize two teams: project team A consisting of 10 people and project team B consisting of 15 people. Also, suppose the employees to be assigned to these project teams are selected from 50 employees listed as candidates. In this case, the group generation device 2000 generates these two teams by appropriately selecting, from the 50 candidates, 10 employees to be assigned to project team A and 15 employees to be assigned to project team B.

[0017] Additionally, the candidates may include people who are not assigned to any group. For example, in the example of "organizing a project team" mentioned above, 40 of the 50 candidates will not be assigned to a group.

[0018] To determine the allocation of candidates to groups, the group generation device 2000 acquires person information 10. The person information 10 indicates information about each candidate. For example, the person information 10 indicates identification information (such as an employee number or name) of each candidate.

[0019] The group generation device 2000 uses the person information 10 to determine candidate persons to be assigned to each of one or more groups. Here, the psychological state index value of each candidate person is used to assign the candidate persons to groups. The psychological state index value is an index value related to a person's psychological state. For example, the psychological state index value represents the degree of goodness of a person's psychological state.

[0020] Furthermore, a generation rule is used to assign candidates to each group. The generation rule represents a criterion for group formation based on the psychological state index value. The group generation device 2000 determines candidates to be assigned to each group based on the psychological state index value of each candidate and the generation rule.

[0021] <Examples of effects> According to the group generation device 2000 of this embodiment, the allocation of candidate persons to groups is determined using the psychological state index value of each candidate person and a generation rule that indicates a criterion for group composition based on the psychological state index value. Therefore, it is possible to assign people to groups taking into account the psychological state of each person.

[0022] The group generation device 2000 of this embodiment will be described in more detail below.

[0023] <Example of functional configuration> 2 is a block diagram illustrating an example of the functional configuration of a group generation device 2000 according to an embodiment. The group generation device 2000 includes an acquisition unit 2020 and a determination unit 2040. The acquisition unit 2020 acquires person information 10. The determination unit 2040 determines the allocation of candidate persons to one or more groups using the psychological state index value of each candidate person indicated in the person information 10 and a generation rule.

[0024] <Example of hardware configuration> Each functional component of the group generation device 2000 may be realized by hardware that realizes each functional component (e.g., a hardwired electronic circuit, etc.), or may be realized by a combination of hardware and software (e.g., a combination of an electronic circuit and a program that controls it). Below, a case where each functional component of the group generation device 2000 is realized by a combination of hardware and software will be further described.

[0025] 3 is a block diagram illustrating an example of the hardware configuration of a computer 1000 that realizes the group generation device 2000. The computer 1000 is any computer. For example, the computer 1000 is a stationary computer such as a PC (Personal Computer) or a server machine. Alternatively, the computer 1000 may be a portable computer such as a smartphone or a tablet terminal. The computer 1000 may be a dedicated computer designed to realize the group generation device 2000, or may be a general-purpose computer.

[0026] For example, by installing a predetermined application on the computer 1000, each function of the group generation device 2000 is realized on the computer 1000. The application is configured with a program for realizing each functional component of the group generation device 2000. Note that any method for acquiring the program may be used. For example, the program may be acquired from a storage medium (such as a DVD disc or USB memory) on which the program is stored. Alternatively, the program may be acquired by downloading the program from a server device that manages the storage device on which the program is stored.

[0027] The computer 1000 has a bus 1020, a processor 1040, a memory 1060, a storage device 1080, an input / output interface 1100, and a network interface 1120. The bus 1020 is a data transmission path for the processor 1040, the memory 1060, the storage device 1080, the input / output interface 1100, and the network interface 1120 to transmit and receive data to and from each other. However, the method for connecting the processor 1040 and the like to each other is not limited to a bus connection.

[0028] The processor 1040 is a processor such as a central processing unit (CPU), a graphics processing unit (GPU), or a field-programmable gate array (FPGA). The memory 1060 is a main storage device realized using a random access memory (RAM) or the like. The storage device 1080 is an auxiliary storage device realized using a hard disk, a solid state drive (SSD), a memory card, or a read only memory (ROM) or the like.

[0029] The input / output interface 1100 is an interface for connecting the computer 1000 to an input / output device. For example, the input / output interface 1100 is connected to an input device such as a keyboard and an output device such as a display device.

[0030] The network interface 1120 is an interface for connecting the computer 1000 to a network. This network may be a LAN (Local Area Network) or a WAN (Wide Area Network).

[0031] Storage device 1080 stores programs (programs that realize the above-mentioned applications) that realize the functional components of group generation device 2000. Processor 1040 reads these programs into memory 1060 and executes them to realize the functional components of group generation device 2000.

[0032] The group generation device 2000 may be realized by one computer 1000, or may be realized by multiple computers 1000. In the latter case, the configurations of the computers 1000 do not need to be the same, and can be different from one another.

[0033] <Processing flow> 4 is a flowchart illustrating a process executed by the group generation device 2000 according to the embodiment. The acquisition unit 2020 acquires the person information 10 (S102). The determination unit 2040 determines candidate persons to be assigned to each of one or more groups based on the psychological state index value of each candidate person and the generation rule (S104).

[0034] <Acquisition of person information 10: S102> The acquisition unit 2020 acquires the personal information 10 (S102). The personal information 10 indicates information about each candidate. As described above, the personal information 10 indicates identification information of each candidate. In addition, for example, the personal information 10 may further indicate information about the attributes of each candidate. The attributes of the candidate are used, for example, when predicting a psychological state index value based on the attributes, as will be described later. Details regarding the attributes of the candidate will be described later.

[0035] There are various specific methods for the acquiring unit 2020 to acquire the personal information 10. For example, the personal information 10 is stored in advance in a storage unit accessible from the group generation device 2000. In this case, the acquiring unit 2020 acquires the personal information 10 by reading the personal information 10 from the storage unit. Alternatively, for example, the acquiring unit 2020 may acquire the personal information 10 by receiving the personal information 10 transmitted from another device.

[0036] The method for generating the person information 10 is arbitrary. For example, suppose all people who belong to a specific organization such as a company are treated as candidates. In this case, the person information 10 can be generated by acquiring information about all people who belong to the organization by any method. Information about people who belong to a specific organization can be obtained from a system used to manage people who belong to the organization, such as an employee database.

[0037] The personal information 10 may be generated so as to display only those individuals who meet certain conditions as candidates. In this case, the personal information 10 can be generated by searching for individuals who meet the certain conditions from, for example, the employee database described above. Various conditions can be used, such as age, gender, current or past department, job title, qualifications, career history, or work experience.

[0038] Alternatively, for example, the personal information 10 may be generated to indicate, as candidate personalities, persons selected by a user of the group generation device 2000. In this case, for example, the user selects any person using the employee database described above. Then, information indicating each selected person is used as the personal information 10.

[0039] Alternatively, for example, the allocation of candidates to each group may be realized by swapping members among multiple groups. In this case, the candidates are existing members of each group. Therefore, information indicating the existing members of each group may be used as the person information 10.

[0040] For example, suppose that members are to be swapped between department A and department B. That is, suppose that some of the existing members of department A are transferred to department B, and some of the existing members of department B are transferred to department A. In this case, the candidates are the existing members of department A and the existing members of department B. Therefore, information indicating the existing members of each of departments A and B can be used as person information 10.

[0041] It should be noted that not all of the existing members may be treated as candidates, but only some of them. For example, suppose that a group contains members who can be transferred to another group and members who cannot be transferred to another group. In this case, the existing members of the group who can be transferred to another group are treated as candidates. Therefore, for example, the person information 10 indicates only the existing members of the group who can be transferred to another group.

[0042] <About the psychological state index> The psychological state index value is an index value related to a person's psychological state. Here, the psychological state index value may be expressed by a single index value related to a person's psychological state, or may be expressed by a combination of multiple index values ​​related to a person's psychological state (for example, a vector in which the index values ​​are listed). In the latter case, each index value included in the psychological state index value represents the person's psychological state from a different perspective.

[0043] For example, the psychological state index value represents the goodness of a person's psychological state. For example, the happiness level can be used as a value representing the goodness of a psychological state.

[0044] A person's happiness level is determined, for example, using the person's answers to questions about the four factors of happiness disclosed in Non-Patent Document 1. Non-Patent Document 1 discloses 16 questions for measuring a person's happiness level. When using the 16 questions about the four factors of happiness disclosed in Non-Patent Document 1, for example, the psychological state index value is expressed as a combination of the answer values ​​to each of these 16 questions (i.e., a combination of 16 index values). However, instead of using the answers to all 16 questions, only the answers to some of the questions may be used.

[0045] Alternatively, the psychological state index value may be expressed as a combination of scores for each of the four happiness factors calculated using answers to questions about the four happiness factors. For example, the 16 questions disclosed in Non-Patent Document 1 include four questions for each of the first to fourth happiness factors. Therefore, for example, the psychological state index value may be a combination of four scores: statistical values ​​of answers to four questions about the first happiness factor, statistical values ​​of answers to four questions about the second happiness factor, statistical values ​​of answers to four questions about the third happiness factor, and statistical values ​​of answers to four questions about the fourth happiness factor. Here, various types of statistical values, such as sums and averages, may be used as the statistical values.

[0046] <About the group> Various groups can be handled as groups to which candidates can be assigned. For example, the meaning of each group is not particularly limited, and only the total number of groups may be specified. In this case, the assignment of candidates to each group corresponds to the process of "dividing the multiple candidates shown in the personal information 10 into a specified number of groups."

[0047] Alternatively, for example, each group to which a candidate is to be assigned may be specified. For example, suppose that the assignment of a candidate to two departments, Department A and Department B, is to be determined. In this case, the groups Department A and Department B are specified.

[0048] For example, the acquisition unit 2020 acquires group information, which is information about groups to which candidates are assigned. For example, the group information indicates the total number of groups. Alternatively, for example, the group information indicates identification information of the groups. Alternatively, for example, if a group has existing members, the group information indicates identification information of the existing members of the group.

[0049] Alternatively, the group information may indicate constraints on the group. For example, the constraints may be conditions on the minimum number of people, the maximum number of people, or both of them. Alternatively, the constraints may be conditions on the attributes of people to be included in the group.

[0050] For example, suppose that levels are treated as attributes, and are set according to a person's abilities and experience. Examples of levels that can be adopted include the level at which a person can work as a manager (hereafter referred to as manager level) and the level at which a person can work as a group leader (hereafter referred to as group leader level). When levels are treated as attributes in this way, group constraints, for example, are conditions regarding the minimum and maximum number of people at each level who can be assigned to the group. For example, it is conceivable to set constraints such as "one or more people at manager level and three or more people at group leader level."

[0051] When multiple groups are handled, the constraints may be common to the multiple groups or may be different for each group. Also, both the common constraints to the multiple groups and the different constraints for each group may be set.

[0052] <Decision on candidate allocation: S104> The determination unit 2040 determines the allocation of candidates to each group based on the psychological state index value of each candidate and the generation rule. The generation rule is a criterion for determining the candidate to be allocated to each group based on the psychological state index value. For example, if the process of determining the candidate to be allocated to each group is considered as a process of solving a combinatorial optimization problem, the generation rule can be expressed as an objective function to be maximized or minimized in the combinatorial optimization problem. Therefore, the candidate to be allocated to each group is determined by combinatorial optimization, in which the candidate is allocated to each group so as to maximize or minimize the objective function.

[0053] When the generation rule is expressed by maximizing or minimizing an objective function, for example, the determination unit 2040 determines the allocation of candidate persons to each group so that the objective function is maximized or minimized, using various algorithms for solving a combinatorial optimization problem. However, the allocation of candidate persons to each group so that the objective function is maximized or minimized, which is determined by the determination unit 2040, does not have to be an exact solution. For example, the determination unit 2040 may determine the allocation of candidate persons to each group so that the objective function is maximized or minimized, using various algorithms for obtaining an approximate optimal solution to a combinatorial optimization problem.

[0054] Furthermore, as will be described later, the generation rules do not necessarily have to be expressed as objective functions.

[0055] Below, some specific examples of generation rules are given.

[0056] <<Example of a production rule 1>> For example, the generation rule may be "Assign both candidate individuals with high psychological state index values ​​and individuals with low psychological state index values ​​to a group." For example, this generation rule can be expressed as a rule to maximize the following objective function U1(G):

number

[0057] G[i] represents a set of identifiers of people included in the i-th group. Note that the group identifiers used in formula (1) and each formula described later are natural numbers that are uniquely assigned to each group, starting from 1. Similarly, in formula (1) and each formula described later, the identifiers of people included in G[i] are natural numbers that are uniquely assigned to each person included in G[i], starting from 1.

[0058] M represents the number of dimensions of the psychological state index value when the psychological state index value is represented by a multidimensional vector. For example, when a vector listing answers to 16 questions regarding the four factors of happiness disclosed in Non-Patent Document 1 is used as the psychological state index value, M=16. When the psychological state index value is represented by a single value, M=1. j is the index of an element in the vector representing the psychological state index value.

[0059] S[i][j] represents the set obtained by extracting only the j-th element value from the psychological state index values ​​of each person in group i. k is the identifier of the person in group i. v[k] represents the psychological state index value of person k. v[k][j] represents the value of the j-th element of the vector v[k] representing the psychological state index value.

[0060] In equation (1), D1(i) is the difference between the maximum and minimum psychological state index values ​​in group i for each element j. Then, the sum of the differences between the maximum and minimum values ​​calculated for each element j is calculated as D1(i). The sum of D1(i) calculated for each group i is then calculated as U1(G).

[0061] The determination unit 2040 determines a set G that maximizes the objective function U1(G). To achieve this, for example, the determination unit 2040 calculates the value of the objective function U1(G) while varying the allocation of candidate persons to each group i (in other words, the identification information of the persons included in G[i]), and compares the calculated values ​​to determine the set G that maximizes the objective function U1(G). Here, various existing algorithms can be used as a specific algorithm for determining the allocation of elements (here, candidate persons) to multiple groups so that the objective function is maximized. Note that if there are existing members in group i and these members cannot be moved to another group, the identifiers of these existing members are always included in G[i].

[0062] By maximizing the objective function defined by equation (1), it is possible to increase the difference between the maximum and minimum values ​​for each element of the psychological state index value in each group. In this way, it is possible to generate teams in which members with good psychological states can support members with bad psychological states.

[0063] Note that the objective function U1(G) can be partially changed without departing from its purpose. For example, U1(G) may be determined by the average of D(i) instead of the sum of D(i). Furthermore, D(i) may be determined by the average of the differences between the maximum and minimum values ​​calculated for each element instead of the sum of the differences between the maximum and minimum values ​​calculated for each element.

[0064] <<Example of a production rule 2>> For example, the generation rule is "reduce the difference between the psychological state index value for the group and the psychological state index value for the whole group." For example, this generation rule can be expressed as a rule to minimize the following objective function U2(G):

number

[0065] In equation (2), D2(i) calculates the absolute value of the difference between the average value of the psychological state index for all groups and the average value of the psychological state index for group i for each element j. Then, the sum of the absolute values ​​of the differences calculated for each element j is calculated as D2(i). Then, the sum of D2(i) calculated for each group i is calculated as U2(G).

[0066] The determination unit 2040 determines a set G that minimizes the objective function U2(G). Here, various existing algorithms can be used as a specific algorithm for determining the allocation of elements (candidates in this case) to multiple groups so that the objective function is minimized. Note that if there are existing members in group i and these members cannot be moved to another group, the identifiers of these existing members are always included in G[i].

[0067] By minimizing this objective function, each group is generated so that the difference between the average psychological index value within the group and the average psychological index value across all groups is small. Therefore, candidate individuals are assigned to each group so that the psychological index values ​​are balanced among the groups.

[0068] The objective function U2(G) can be partially modified without departing from its purpose. For example, the objective function U2(G) may use an average instead of a sum. Furthermore, the absolute value of D2(i) may be replaced by a square.

[0069] <<Example of Generation Rule 3>> For example, the generation rule may be "increase the difference between the psychological state index value for the group and the psychological state index value for the whole group." For example, such a generation rule can be expressed as a rule to maximize the following objective function U3(G):

[0070]

number

[0071] The determination unit 2040 determines a set G that maximizes the objective function U3(G). As described above, various existing algorithms can be used as a specific algorithm for determining the allocation of elements to multiple groups so as to minimize the objective function. Note that if there are existing members in group i and these members cannot be moved to another group, the identifiers of these existing members are always included in G[i].

[0072] According to formula (3), groups are generated that have the characteristic that "the average psychological state index value within the group is greater than the average psychological state index value for the whole group." This makes it possible to generate strong groups with a high degree of favorable psychological states.

[0073] The objective function U3(G) can be partially modified without departing from its purpose. For example, the objective function U3(G) may use an average instead of a sum.

[0074] <<Example of production rule 4>> For example, the rule to be generated is "to make the variance of the psychological state index value within a group close to the variance of the psychological state index value overall." This generation rule can be expressed as a rule to minimize the following objective function U4(G), for example.

number

[0075] The determination unit 2040 determines a set G that minimizes the objective function U4(G). As described above, various existing algorithms can be used as a specific algorithm for determining the allocation of elements to multiple groups so that the objective function is minimized. Furthermore, if there are existing members in group i and these members cannot be moved to another group, the identifiers of these existing members are always included in G[i].

[0076] According to the generation rule expressed by formula (4), the variation in the psychological state index value can be made to be the same for each group, which prevents the generation of groups with extremely large variations in psychological states among members.

[0077] The objective function U4(G) can be partially modified without departing from its purpose. For example, the objective function U4(G) may use an average instead of a sum. Furthermore, the objective function D4(i) may use a square instead of an absolute value.

[0078] <<Example of Production Rule 5>> Another example of a generation rule is "allocate people with high psychological state index values ​​and people with low psychological state index values ​​equally to each group." The allocation of candidate people according to this generation rule can be represented, for example, by the procedure shown in Fig. 5. Fig. 5 is a flowchart showing the procedure for allocating candidate people according to example generation rule 5.

[0079] The determining unit 2040 generates a permutation P in which the candidate persons are sorted in descending or ascending order of their mental state index values ​​(S202). The determining unit 2040 initializes the identifier i of the group to be processed to 1 (S204).

[0080] S206 to S214 form loop processing L1, which is executed for each group. In S206, the determination unit 2040 determines whether the identifier of the group to be processed is equal to or less than the total number N of groups (i.e., whether i<=N). If i<=N is not true, loop processing L1 ends, and the processing in FIG. 5 proceeds to S2204. On the other hand, if i<=N is true, the processing in FIG. 5 proceeds to S208.

[0081] In S208, the determination unit 2040 extracts the first a and last b people from the permutation P and assigns them to group i. Here, a and b may be the same value or different values.

[0082] The determining unit 2040 determines whether or not there are still candidates remaining in the permutation P (S210). If there are no candidates remaining in the permutation P (S210: NO), the processing in FIG. 5 ends.

[0083] On the other hand, if a candidate remains in the permutation P (S210: YES), the determining unit 2040 adds 1 to i (S212). Next, since S214 is the end of the loop processing L1, the processing in FIG. 5 proceeds to S206.

[0084] In this way, according to the series of processes shown in the flowchart of Figure 5, the process of "assigning the first a person and the last b person of permutation P to each group" is repeated until permutation P is empty. Therefore, people with large psychological state index values ​​and people with small psychological state index values ​​are assigned to each group in order. By assigning candidate people in this way, it is possible to balance the psychological states of each group.

[0085] <Method for identifying psychological state index values> To determine the allocation of candidates to groups using the above-described method, it is necessary to identify the psychological state index value v[k] for each candidate k. For example, the psychological state index value is predetermined for each candidate. In this case, the determination unit 2040 uses the predetermined psychological state index value for each candidate. For example, in this case, the personal information 10 indicates, for each candidate, the candidate's identification information and the psychological state index value in association with each other.

[0086] The method for determining the psychological state index value in advance is arbitrary. For example, the psychological state index value may be determined using answers to questions about the four factors of happiness described above. In this case, for example, a questionnaire containing questions about the four factors of happiness may be sent to each candidate (e.g., each employee), and answers to the questionnaire may be collected from each candidate, thereby calculating the psychological state index value for each candidate.

[0087] Here, the psychological state index value of each candidate person may be calculated from the results of a single questionnaire (for example, the results of the most recent questionnaire), or may be calculated from the results of multiple questionnaires. In the latter case, for example, the above-mentioned questionnaire is sent to each person periodically. This makes it possible to calculate the psychological state index value for each person from each of the multiple questionnaires. Therefore, for example, as the psychological state index value for each person, a statistical value of multiple psychological state index values ​​obtained from multiple questionnaires is used. For example, if a questionnaire is conducted once a week, one possible method is to calculate the psychological state index value for each person using the results of the most recent four questionnaires.

[0088] The psychological state of each candidate may change depending on the group to which they are assigned. For example, a person's psychological state is likely to be significantly different when they are assigned to a group with a boss they do not get along with than when they are assigned to a group with a boss they get along with. Also, a person's psychological state is likely to be significantly different when they are assigned to a department related to a job they desire than when they are assigned to a department related to a job they do not desire.

[0089] Therefore, for example, when trying various patterns of assigning candidates to each group, the determining unit 2040 may calculate the psychological state index value of each candidate in each trial based on the composition of the group in that trial. In this case, for example, an estimation model for estimating the psychological state index value of each candidate is determined in advance. The estimation model can be realized by any machine learning model such as a neural network.

[0090] An estimation model for estimating a psychological state index value of a certain person is configured to output the psychological state index value of the certain person in response to input of information representing the environment in which the certain person is placed. The environment of the certain person is represented, for example, by the attributes of the certain person, the attributes of the group to which the certain person belongs, or the attributes of each person in the same group as the certain person. Here, the attributes of the certain person representing the environment of the certain person, such as job or occupation, are used. The attributes of the certain group can be information representing the characteristics of the certain group, such as the location of the group's office or the frequency of overtime or holiday work in the group. The attributes of each person in the same group can be various, such as personality, age, gender, current or past department, position, qualifications, career history, or work experience.

[0091] To generate an estimation model for each person, for example, the history of each person's past psychological state index values ​​is recorded in association with information describing the environment in which the person was placed when the psychological state index value was calculated. The estimation model is trained using multiple training data consisting of pairs of "input data = information describing the environment, ground truth data = psychological state index value."

[0092] <Consideration of constraints> When the group information indicates constraints on the groups, the determination unit 2040 determines the allocation of candidate persons to groups, taking into consideration the constraints indicated in the group information in addition to the generation rules. Here, various existing algorithms can be used as a specific algorithm for determining elements (candidate persons in this disclosure) to be allocated to each group so as to maximize or minimize the objective function while taking into consideration the constraints.

[0093] Furthermore, as shown in Example 5 of the generation rule, even when an objective function is not used, various methods can be used to assign candidates taking into account constraints. For example, suppose the constraints are conditions related to the attributes of the people. In this case, when extracting candidates from permutation P in S208, the determination unit 2040 prioritizes extracting people who satisfy the constraints. For example, suppose a constraint is set that "each group must include at least one manager-level person." In this case, for example, before executing loop process L1, the determination unit 2040 sequentially executes the process of "extracting from permutation P the manager-level candidate closest to the beginning of permutation P and adding it to the group" for each group.

[0094] <Result output> The group generation device 2000 outputs output information representing the processing result. For example, the output information indicates, for each of one or more groups, the identification information of the group and the identification information of each candidate assigned to the group, in association with each other.

[0095] Alternatively, for example, if the psychological state index value of each candidate person is estimated according to the group to which the candidate person is assigned, the output information may further indicate the estimated psychological state index value for each candidate person assigned to the group. Furthermore, the output information may indicate, for each group, statistical information regarding the psychological state index value in the group. The statistical information regarding the psychological state index value indicates, for example, the maximum value, minimum value, average, or variance of the psychological state index value in the group.

[0096] The output information may be output in any manner. For example, the output information may be stored in any storage unit. Alternatively, for example, the output information may be displayed on any display device. Alternatively, for example, the output information may be transmitted to any device.

[0097] <Example of Operation Screen Provided by Group Generation Device 2000> Here, an example of an operation screen provided by the group generation device 2000 is shown. Here, the functional configuration unit that outputs the operation screen is called a screen output unit. Fig. 6 is a diagram showing an example of an operation screen provided by the group generation device 2000. The operation screen 100 in Fig. 6 has a display area 110, a display area 120, a display area 130, and a display area 140.

[0098] In this example, the group generation device 2000 is used to assign personnel to each group under a business division named Business Division A. For example, a user of the group generation device 2000 selects a business for which the user wishes to change the personnel assignment from among multiple business divisions owned by a company. In response to this selection, the group generation device 2000 acquires person information 10 indicating all people belonging to the selected business division as candidate people. The group generation device 2000 also acquires group information indicating information about each group under the selected business division. The group generation device 2000 then assigns each person belonging to the selected business division to one of the groups under the selected business division.

[0099] In this example, the target is Business Division A. Therefore, the group generation device 2000 assigns each person belonging to Business Division A to one of Group G1, Group G2, and Group G3 under Business Division A. As a result of the assignment, the group generation device 2000 outputs the operation screen 100 of FIG.

[0100] Display area 110 shows the average value of the psychological state index values ​​of all persons calculated for the entire A division (i.e., all groups under A division). Here, the psychological state index value of each person is estimated using the estimation model described above. In this example, the psychological state index value is represented by the sum of the scores calculated for each of the four happiness factors disclosed in Non-Patent Document 1. In display area 110 and display area 120, the graphs showing the magnitude of the psychological state index value are the sum of the scores for each factor.

[0101] In the display area 110, "original plan" indicates the average value of the psychological state index values ​​of all people in the case where personnel allocation is performed as determined by the group generation device 2000. "comparison plan" indicates the average value of the psychological state index values ​​of all people in the case where personnel allocation is changed by user input operations, as will be described later. User input operations will be described later. "current" indicates the average value of all psychological state index values ​​for the current actual group composition. "previous" indicates the average value of all psychological state index values ​​for past actual group compositions.

[0102] Display area 120 shows the composition of Business Division A. By selecting two groups displayed in the display area, the user can swap members between the two selected groups. The "comparison plan" mentioned above shows information about cases in which personnel allocation is changed by this swap operation.

[0103] The display area 130 shows, for each of the two selected groups, the average value of the psychological state index values ​​of all the people belonging to that group.

[0104] The user can try changing the personnel allocation by performing the above-mentioned replacement operation after viewing the composition of the group automatically generated by the group generation device 2000. Then, by comparing the psychological state index value before the trial (original plan) with the psychological state index value after the trial (comparison plan), the user can determine whether it is appropriate to change the personnel allocation automatically generated by the group generation device 2000.

[0105] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

[0106] In the above examples, the program includes instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more functions described in the embodiments. The program may be stored in a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, computer-readable media or tangible storage media include random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technology, CD-ROM, digital versatile disc (DVD), Blu-ray (registered trademark) disc or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device. The program may also be transmitted on a transitory computer-readable medium or communication medium. By way of example and not limitation, transitory computer-readable media or communication media include electrical, optical, acoustic, or other forms of propagated signals.

[0107] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes. (Appendix 1) an acquisition means for acquiring personal information regarding a plurality of candidate persons; A group generation device having a determination means for determining the allocation of the candidate persons to each of one or more groups based on a psychological state index value related to the psychological state of each candidate person and a group formation rule based on the psychological state index value. (Appendix 2) 2. The group generation device according to claim 1, wherein the person information indicates the psychological state index value of each of the candidate persons. (Appendix 3) an estimation model is defined that estimates the mental state index value of the candidate person according to the environment of the group to which the candidate person belongs; The group generation device according to claim 1, wherein the determination means calculates the psychological state index value of each of the candidate persons using the estimation model. (Appendix 4) 4. The group generation device according to claim 1, wherein the rule is a rule for maximizing or minimizing an objective function calculated from the psychological state index value of each person included in each of the groups. (Appendix 5) the personal information includes information on attributes of each of the candidate personalities; The group generation device according to any one of Supplementary Notes 1 to 3, wherein the determination means determines the allocation of the candidate persons to each of the groups so as to satisfy constraints on attributes of the candidate persons to be included in each of the groups. (Appendix 6) a screen output means for outputting an operation screen showing, for each of the first group and the second group, the statistical values ​​of the psychological state index values ​​of the persons included in the group and information of each person included in the group; The group generation device according to any one of appendices 1 to 3, wherein the screen output means, in response to an operation of swapping a person included in the first group with a person included in the second group, displays on the operation screen a statistical value of the psychological state index value of the person included in the first group after the swapping operation and a statistical value of the psychological state index value of the person included in the second group after the swapping operation. (Appendix 7) an acquisition step of acquiring personal information regarding a plurality of candidate persons; A computer-implemented group generation method comprising: a determination step of determining the allocation of the candidate persons to each of one or more groups based on a psychological state index value relating to the psychological state of each candidate person and a group formation rule based on the psychological state index value. (Appendix 8) 8. The group generation method according to claim 7, wherein the person information indicates the mental state index value of each of the candidate persons. (Appendix 9) an estimation model is defined that estimates the mental state index value of the candidate person according to the environment of the group to which the candidate person belongs; 8. The group generation method according to claim 7, wherein in the determining step, the mental state index value of each of the candidate persons is calculated using the estimation model. (Appendix 10) 10. The group generation method according to any one of appendices 7 to 9, wherein the rule is a rule for maximizing or minimizing an objective function calculated from the psychological state index value of each person included in each of the groups. (Appendix 11) the personal information includes information on attributes of each of the candidate personalities; A group generation method according to any one of appendices 7 to 9, wherein in the determination step, the allocation of the candidate individuals to each of the groups is determined so as to satisfy constraints on the attributes of the candidate individuals to be included in each of the groups. (Appendix 12) a screen output step of outputting, for each of the first group and the second group, an operation screen showing statistics of the psychological state index values ​​of the persons included in the group and information of each person included in the group; The group generation method according to any one of Appendices 7 to 9, wherein in the screen output step, in response to an operation of swapping a person included in the first group with a person included in the second group, statistical values ​​of the psychological state index values ​​of the person included in the first group after the swapping operation and statistical values ​​of the psychological state index values ​​of the person included in the second group after the swapping operation are displayed on the operation screen. (Appendix 13) an acquisition step of acquiring personal information regarding a plurality of candidate persons; A non-transitory computer-readable medium storing a program that causes a computer to execute the steps of: determining the allocation of the candidate person to each of one or more groups based on a psychological state index value related to the psychological state of each candidate person and a group composition rule based on the psychological state index value; (Appendix 14) 14. The computer-readable medium of claim 13, wherein the person information indicates the psychological state index value of each of the candidate people. (Appendix 15) an estimation model is defined that estimates the mental state index value of the candidate person according to the environment of the group to which the candidate person belongs; 14. The computer-readable medium of claim 13, wherein the determining step uses the estimation model to calculate the psychological state index value for each of the candidate persons. (Appendix 16) 16. A computer-readable medium according to any one of appendices 13 to 15, wherein the rule is a rule for maximizing or minimizing an objective function calculated from the psychological state index values ​​of each person included in each of the groups. (Appendix 17) the personal information includes information on attributes of each of the candidate personalities; 16. The computer-readable medium of any one of claims 13 to 15, wherein in the determining step, the allocation of the candidates to each of the groups is determined so as to satisfy constraints on attributes of the candidates to be included in each of the groups. (Appendix 18) a screen output step of outputting, for each of the first group and the second group, an operation screen showing statistics of the psychological state index values ​​of the persons included in the group and information of each person included in the group; A computer-readable medium as claimed in any one of Appendices 13 to 15, wherein in the screen output step, in response to an operation of swapping a person included in the first group with a person included in the second group, statistical values ​​of the psychological state index values ​​of the person included in the first group after the swapping operation and statistical values ​​of the psychological state index values ​​of the person included in the second group after the swapping operation are displayed on the operation screen. [Explanation of symbols]

[0108] 10 Personal information 100 operation screen 110 Display Area 120 display area 130 display area 140 display area 1000 computers 1000 each computer 1020 Bus 1040 processor 1060 memory 1080 storage device 1100 Input / Output Interface 1120 Network Interface 2000 Group Generator 2020 Acquisition Department 2040 Decision Section

Claims

1. an acquisition means for acquiring personal information regarding a plurality of candidate persons; a determination means for determining allocation of the candidate persons to each of one or more groups based on a psychological state index value relating to the psychological state of each candidate person and a group composition rule based on the psychological state index value; the person information indicates the mental state index value of each of the candidate persons, the rule is a rule for maximizing or minimizing an objective function calculated from the mental state index value of each person included in each of the groups, a screen output means for outputting an operation screen showing, for each of the first group and the second group, the statistical values ​​of the psychological state index values ​​of the persons included in the group and information on each person included in the group; the screen output means, in response to an operation of swapping the persons included in the first group with the persons included in the second group, displays on the operation screen the statistical values ​​of the psychological state index values ​​of the persons included in the first group after the operation and the statistical values ​​of the psychological state index values ​​of the persons included in the second group after the operation.

2. an estimation model is defined that estimates the mental state index value of the candidate person according to the environment of the group to which the candidate person belongs; The group generation device according to claim 1 , wherein the determining means calculates the psychological state index value of each of the candidate persons by using the estimation model.

3. the personal information includes information on attributes of each of the candidate personalities; 3. The group generating device according to claim 1, wherein the determining means determines the allocation of the candidates to the groups so as to satisfy constraints on attributes of the candidates to be included in each group.

4. an acquisition step of acquiring personal information regarding a plurality of candidate persons; a determining step of determining allocation of the candidate persons to each of one or more groups based on a psychological state index value relating to the psychological state of each candidate person and a group composition rule based on the psychological state index value, the person information indicates the mental state index value of each of the candidate persons, the rule is a rule for maximizing or minimizing an objective function calculated from the mental state index value of each person included in each of the groups, a screen output step of outputting, for each of the first group and the second group, an operation screen showing statistics of the psychological state index values ​​of the persons included in the group and information of each person included in the group; a group generation method executed by a computer, in which, in the screen output step, in response to an operation of swapping a person included in the first group with a person included in the second group, statistical values ​​of the psychological state index values ​​of the person included in the first group after the operation and statistical values ​​of the psychological state index values ​​of the person included in the second group after the operation are displayed on the operation screen.

5. an acquisition step of acquiring personal information regarding a plurality of candidate persons; a determining step of determining allocation of the candidate persons to each of one or more groups based on a psychological state index value relating to the psychological state of each candidate person and a group composition rule based on the psychological state index value; the person information indicates the mental state index value of each of the candidate persons, the rule is a rule for maximizing or minimizing an objective function calculated from the mental state index value of each person included in each of the groups, causing the computer to execute a screen output step of outputting an operation screen showing, for each of the first group and the second group, statistics of the psychological state index values ​​of the persons included in the group and information of each person included in the group; In the screen output step, in response to an operation of swapping a person included in the first group with a person included in the second group, a program displays on the operation screen the statistical values ​​of the psychological state index values ​​of the person included in the first group after the operation and the statistical values ​​of the psychological state index values ​​of the person included in the second group after the operation.

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