Tissue condition identification device, tissue condition identification method, program, and recording medium

Abstract

To provide an organization state identification apparatus configured to identify a state of an organization.SOLUTION: An organization state identification apparatus 10 includes: an appreciation data acquisition unit 11 which acquires appreciation data transmission history information indicating that one constituent member has sent appreciation data to another constituent member; a network generation unit 12 which generates a constituent member network in an organization to which the constituent members belong, on the basis of the appreciation data transmission history information; a network feature quantity analysis unit 13 which analyzes feature quantity of the constituent member network; and an organization state identification unit 14 which identifies an organization state on the basis of the analyzed feature quantity of the constituent member network.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a tissue condition identification device, a tissue condition identification method, a program, and a recording medium. [Background technology]

[0002] In general, it is said that Japanese people are not good at expressing gratitude to others directly. However, it has also been reported that expressing gratitude to others in companies and other organizations has a positive effect on performance.

[0003] For example, Patent Document 1 proposes a system for building trust among members of an organization through a computer network. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2006-40253 A Summary of the Invention [Problem to be solved by the invention]

[0005] In organizations made up of multiple members, there is a demand for technology that can grasp the state of the organization.

[0006] Therefore, an object of the present invention is to provide a tissue condition identification device, a tissue condition identification method, a program, and a recording medium that are capable of grasping the state of tissue. [Means for solving the problem]

[0007] In order to achieve the above object, the tissue condition identification device of the present invention comprises: The system includes a gratitude data acquisition unit, a network generation unit, a network feature analysis unit, and an organization state identification unit, the thank you data acquisition unit acquires thank you data transmission history information indicating that thank you data has been transmitted from one member to another member; The network generation unit generates a member network within an organization to which the member belongs based on the gratitude data transmission history information, The network feature analysis unit analyzes features of the member network, The organization state identification unit is a device that identifies an organization state based on the feature amount of the analyzed member network.

[0008] The tissue condition identification method of the present invention comprises the steps of: The method includes a gratitude data acquisition step, a network generation step, a network feature analysis step, and an organizational state identification step. The thank you data acquisition step acquires thank you data transmission history information indicating that thank you data has been transmitted from one member to another member, The network generating step generates a member network within an organization to which the member belongs based on the gratitude data transmission history information, The network feature analysis step includes analyzing a feature of the member network, The organizational state identifying step is a method for identifying an organizational state based on a feature amount of the analyzed member network. Effect of the Invention

[0009] According to the present invention, it is possible to grasp the state of an organization made up of multiple members. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a block diagram showing a configuration of an example of the apparatus according to the first embodiment. [Diagram 2] FIG. 2 is a block diagram showing an example of a hardware configuration of the apparatus according to the first embodiment. [Diagram 3] FIG. 3 is a flowchart showing an example of processing in the device of the first embodiment. [Figure 4]FIG. 4(A) is a diagram showing an example of thank you data transmission history information, and FIG. 4(B) is a diagram showing an example of skill data. [Diagram 5] FIG. 5 is a diagram showing an example of the generated member network. [Figure 6] FIG. 6 is a diagram showing an example of analyzing the feature quantities of a member network. [Figure 7] FIG. 7 is a diagram illustrating an example of calculation of betweenness centrality. [Figure 8] FIG. 8 illustrates an example of calculation of the number of complete graphs. [Figure 9] FIG. 9 is a diagram illustrating an example of calculation of the average shortest distance. [Figure 10] FIG. 10 is a schematic diagram showing an example of matching. [Figure 11] FIG. 11 is a block diagram showing a configuration of an example of the apparatus according to the third embodiment. [Figure 12] FIG. 12 is a flowchart showing an example of processing in the device of the third embodiment. [Figure 13] FIG. 13 is a schematic diagram of a display unit in the device of the third embodiment. [Figure 14] FIG. 14 is a schematic diagram of a display unit in the device of the third embodiment. [Figure 15] FIG. 15 is a schematic diagram of a display unit in the device of the third embodiment. [Figure 16] FIG. 16 is a schematic diagram of a display unit in the device of the third embodiment. [Figure 17] FIG. 17 is a schematic diagram of a display unit in the device of the third embodiment. [Figure 18] FIG. 18 is a schematic diagram of a display unit in the device of the third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] In the tissue condition identification device of the present invention, for example, The network feature analysis unit further determines a network type of the member network from the feature, The organizational state identifying unit may identify the organizational state based on the network type.

[0012] The tissue condition identification device of the present invention includes, for example, Further, the device includes a skill data acquisition unit and a matching unit, The skill data acquisition unit acquires skill data for each instructor, The matching unit may be configured to match the organization with an instructor who will instruct the organization from among the plurality of instructors, based on the skill data and the organization state.

[0013] In the tissue condition identifying method of the present invention, for example, The network feature analysis step further includes determining a network type of the member network from the feature, The tissue state identifying step may include identifying the tissue state based on the network type.

[0014] The tissue condition identification method of the present invention includes, for example, Further, the method includes a skill data acquisition step and a matching step, The skill data acquisition step acquires skill data for each instructor, The matching step may be a step of matching the organization with an instructor who will instruct the organization from among the plurality of instructors, based on the skill data and the state of the organization.

[0015] The program of the present invention is a program for causing a computer to execute each step of the method of the present invention as a procedure.

[0016] The recording medium of the present invention is a computer-readable recording medium having the program of the present invention recorded thereon.

[0017] In the present invention, "member" means a member of an organization, and the organization is not particularly limited, and can be, for example, an organization composed of multiple members. Specific examples of the member include athletes, employees, and students. Examples of the organization include companies, unions, government agencies, hospitals, sports teams, clubs, and schools. In the present invention, "leader" means a person who leads an organization, and can be, for example, a coach, instructor, teacher, advisor, consultant, etc.

[0018] Next, an embodiment of the present invention will be described with reference to the drawings. The present invention is not limited to the following embodiment. In each of the drawings, the same parts are given the same reference numerals. In addition, the explanations of each embodiment can be mutually incorporated unless otherwise specified, and the configurations of each embodiment can be combined unless otherwise specified.

[0019] [Embodiment 1] Fig. 1 is a block diagram showing an example of the configuration of an organization state identification device 10 according to the present embodiment. As shown in Fig. 1, the device 10 includes a gratitude data acquisition unit 11, a network generation unit 12, a network feature amount analysis unit 13, and an organization state identification unit 14. The device 10 may or may not further include a skill data acquisition unit 15 and a matching unit 16 as optional configurations.

[0020] The device 10 may be, for example, a single device including each of the above-mentioned units, or a device to which each of the above-mentioned units can be connected via a communication line network. The device 10 may be connected to an external device described later via the communication line network. The communication line network is not particularly limited, and a known network may be used, for example, wired or wireless. Examples of the communication line network include an Internet line, a WWW (World Wide Web), a telephone line, a LAN (Local Area Network), a SAN (Storage Area Network), and a DTN (Delay Tolerant Networking). Examples of wireless communication include WiFi (Wireless Fidelity) and Bluetooth (registered trademark). The wireless communication may be either a form in which each device directly communicates (Ad Hoc communication) or an indirect communication via an access point. The device 10 may be, for example, incorporated in a server as a system. The device 10 may be, for example, a personal computer (PC, for example, desktop type, notebook type) or a terminal (smartphone, tablet terminal) in which the program of the present invention is installed. Furthermore, the present device 10 may be in the form of cloud computing or edge computing, for example, in which at least one of the above-mentioned units is on a server and the other units are on a terminal.

[0021] 2 illustrates a block diagram of the hardware configuration of the device 10. The device 10 includes, for example, a central processing unit (CPU, GPU, etc.) 101, a memory 102, a bus 103, a storage device 104, an input device 105, a display device 106, and a communication device 107. The components of the device 10 are connected to each other via the bus 103 and their respective interfaces (I / F).

[0022] The central processing unit 101 is responsible for the overall control of the device 10. In the device 10, the central processing unit 101 executes, for example, the program of the present invention and other programs, and also reads and writes various information. Specifically, for example, the central processing unit 101 functions as a gratitude data acquisition unit 11, a network generation unit 12, a network feature analysis unit 13, and an organization state identification unit 14. The central processing unit 101 also functions as, for example, a skill data acquisition unit 15 and a matching unit 16.

[0023] The bus 103 can also be connected to, for example, an external device. Examples of the external device include an external storage device (external database, etc.), a printer, etc. The present device 10 can be connected to an external network (the above-mentioned communication line network) by, for example, a communication device 107 connected to the bus 103, and can also be connected to other devices via the external network.

[0024] An example of the memory 102 is a main memory (primary storage device). When the central processing unit 101 performs processing, the memory 102 reads various operation programs, such as the program of the present invention, stored in the storage device 104 described below, and the central processing unit 101 receives data from the memory 102 and executes the program. The main memory is, for example, a RAM (random access memory). The memory 102 may also be, for example, a ROM (read only memory).

[0025] The storage device 104 is also called an auxiliary storage device, for example, in contrast to the main memory (primary storage device). As described above, the storage device 104 stores an operation program including the program of the present invention. The storage device 104 may be, for example, a combination of a recording medium and a drive for reading and writing from and to the recording medium. The recording medium is not particularly limited, and may be, for example, an internal or external type, such as a hard disk (HD), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, or memory card. The storage device 104 may be, for example, a hard disk drive (HDD) in which a recording medium and a drive are integrated, or a solid state drive (SSD).

[0026] In the present device 10, the memory 102 and storage device 104 can also store user log information as well as information obtained from an external database (not shown).

[0027] The device 10 may further include, for example, an input device 105 and a display device 106. The input device 105 is, for example, a touch panel, a keyboard, a mouse, etc. The input device 105 may be, for example, in the form of a software keyboard displayed on the display device 106. The display device 106 may be, for example, an LED display, a liquid crystal display, a touch display, etc.

[0028] Next, an example of the tissue condition identification method of this embodiment will be described with reference to the flowchart of Fig. 3. The tissue condition identification method of this embodiment is carried out as follows, for example, using the tissue condition identification device 10 of Fig. 1. It should be noted that the tissue condition identification method of this embodiment is not limited to use of the tissue condition identification device 10 of Fig. 1.

[0029] First, the thank-you data acquisition unit 11 acquires thank-you data transmission history information indicating that one member has transmitted thank-you data to another member (S11). The thank-you data may be, for example, data expressing gratitude, and may include, for example, the type of gratitude (for example, "thank you," "sorry," "praise," "respect," "happy," etc.), the degree of gratitude, and a thank-you comment. The format of the thank-you data is not particularly limited, and may be, for example, a character, a sentence, an illustrated image, etc. The thank-you data transmission history information may include information such as the transmission date and time of the thank-you data in addition to information indicating from whom the thank-you data was transmitted to whom. For example, the thank-you data acquisition unit 11 may acquire, as the thank-you data transmission history information, the thank-you data itself, statistical information described later, or log information from a terminal transmitting the thank-you data. The format of the acquisition may be, for example, via the communication line network, from a database or server that stores the thank-you data, or from a thank-you transmission device described later, which is a terminal of the member (also called a member terminal). Specifically, the transmission of the thank you data from one member to another member will be described later in embodiment 3. The thank you data transmission history information acquired by the device 10 may be output to an external database or server via the communication line network, or may be stored in a storage unit such as the memory 102 and the storage device 104.

[0030] Next, the network generation unit 12 generates a member network within the organization to which the member belongs based on the gratitude data transmission history information (S12). The member network is, for example, data showing relationships (e.g., intimate relationships) between multiple members within the organization, and is also called a correlation diagram. The form of the member network is not particularly limited, and may be, for example, map information in which a line connects one member who transmitted the gratitude data and another member who received the gratitude data among multiple members.

[0031] Next, the network feature analysis unit 13 analyzes the feature of the member network (S13). The feature is not particularly limited, and examples thereof include an index of centrality in the member network (betweenness centrality, degree centrality, closeness centrality, eigenvector centrality, etc.), the number of complete graphs, and the average shortest distance. The feature to be analyzed may be, for example, one or more. In addition, the network feature analysis unit 13 may further determine the network type of the member network from the feature. Examples of the network type include a distributed type, a silo type, a bridge type, and a bonding type. The distributed type member network indicates, for example, an organizational state in which the connections between nodes (members) are weak or there is no connection at all. The silo type member network indicates, for example, an organizational state in which there are a plurality of smaller organizations within an organization, and there is no connection between the smaller organizations. The bridge-type member network, for example, like the silo-type, includes small organizations within an organization, but indicates an organizational state in which these small organizations are connected to each other. The bonding-type member network, for example, indicates an organizational state in which small organizations are not formed within an organization, and there are many connections between nodes (members) (i.e., there are many nodes with high degrees). More specific details will be described later. This makes it possible to quantitatively express the organizational state described later.

[0032] Then, the organizational state identification unit 14 identifies the organizational state based on the analyzed feature amount of the member network (S14), and ends the process (END). The organizational state identification unit 14 may also identify the organizational state based on the network type. The organizational state is not particularly limited, and examples include the formation stage, confusion stage, unification stage, functioning stage, and dissolution stage in the Tuckman model. The formation stage is, for example, a stage in which the members who will belong to the organization are decided and the members are waiting to see what happens to each other. The confusion stage is, for example, a stage in which opinions, principles, and assertions clash, and the members are wary of each other. The unification stage is, for example, a stage in which the goal is shared by the members and the rules to be followed as an organization are established. The functioning stage is, for example, a stage in which each member acts autonomously and produces further results. The dissolution stage is, for example, a state in which the organization is dissolved due to the achievement of the goal or time constraints, and each member starts moving toward a different goal. Hereinafter, the tissue state will be described using each stage of the Tuchman model, but other models may be used without being limited thereto. Specifically, the tissue state identification unit 14 may identify the tissue state into at least four stages (e.g., formation stage, disorganization stage, integration stage, and function stage, etc.) as described later.

[0033] In this embodiment, for example, the skill data acquiring unit 15 may further acquire skill data for each instructor (S15). The skill data is data indicating the skills of the instructor. Specifically, the skill data includes, for example, skills such as the ability to listen to members and draw out their true feelings (listening skills), the ability to ask members questions so that they think for themselves (questioning skills), and the ability to acknowledge the actions and results of members, i.e., the ability to encourage members to act by acknowledging their existence (cognitive skills), as well as data indicating the instructor's experience, the instructor's field of expertise, and the like. The skill data may also include, for example, data indicating the organizational state in which the instructor is good at teaching. Furthermore, the skill data may be linked to, for example, date and time information when the skill data was acquired. The skill data acquiring unit 15 may acquire the skill data from the instructor's terminal (also called the instructor's terminal) via the communication line network, or from a database or server that stores the skill data. The skill data acquiring unit 15 may acquire the skill data by, for example, asking the instructor a questionnaire about the skill data and inputting the answer to the questionnaire. The format of the questionnaire and its answers is not particularly limited, and for example, paper, a website, etc. can be used. The skill data acquired by the device 10 may be output to an external database or server via the communication line network, or may be stored in a storage unit such as the memory 102 and the storage device 104. The skill data acquisition unit 15 may acquire identification information of the instructor, for example, linked to the skill data. The identification information is not particularly limited, and may be, for example, the name of the instructor, the instructor's ID (identification), the instructor's terminal ID, etc. The step (S15) may be processed in parallel with the steps (S11) to (S14), or may be processed in order. The order is not particularly limited.

[0034] When acquiring the skill data, the present embodiment further performs matching between the organization and the instructor who will instruct the organization from among the multiple instructors based on the skill data and the organization state by the matching unit 16 (S16), and ends the process (END). More specifically, this will be described later.

[0035] According to this embodiment, a member network is generated from thank you data transmission history information, so the state of an organization can be understood. With the transmission history of tools such as email and chat, it is unclear whether the interpersonal relationships between members are good or not. However, according to this embodiment, since it is based on thank you data transmission history information, it can be assumed that the interpersonal relationships between members are good. In other words, by using the thank you data transmission history information, the degree of intimacy between members within an organization can be understood.

[0036] The instructors have different skill data (strengths and weaknesses in instruction methods) for each instructor. Also, the appropriate instruction method for the organization differs depending on the organizational state. Therefore, when the organizational state of the organization to be instructed requires skill data that the instructor is not good at, the instructor has a problem of having to take time and effort to understand the current state of the organization, and the organization and its members cannot receive the effective instruction. However, according to the present embodiment, for example, the skill data and the organizational state are further used, so that appropriate matching between the instructor and the organization is possible.

[0037] [Embodiment 2] Specifically, an example of grasping an organizational state and matching will be described using the device 10. In the following, an example of the members will be players, the organization will be a team, and the instructor will be a coach, but as mentioned above, the present invention is not limited to these.

[0038] First, an example of grasping the organizational state will be described. Each player in a team uses, for example, a thank you transmission device described below to transmit thank you data to other players in the team. Then, the thank you data acquisition unit 11 of the device 10 acquires the thank you data transmission history information from the thank you transmission device. Figure 4(A) shows an example of the thank you data transmission history information. In Figure 4(A), "From" indicates the player who transmitted the thank you data, and "Target" indicates the player to whom the thank you data was transmitted.

[0039] Next, as shown in FIG. 5, the network generation unit 12 reads the gratitude data transmission history information and generates a member network (also called networking). In the member network, the distance and direction between each node (each player) are not particularly limited, and may or may not be calculated according to the feature amount described below. The distance between each node (each player) can be processed, for example, as the degree of gratitude in the gratitude data as the distance between nodes. The degree of gratitude will be described later. Hereinafter, in the member network, the players are also called nodes.

[0040] Next, as shown in FIG. 6, the network feature analysis unit 13 analyzes the feature of the member network, and determines the network type of the member network from the feature. In FIG. 6, the betweenness centrality, the number of complete graphs, and the average shortest distance are obtained as the feature, but as described above, the feature is not limited to these. Specifically, the network feature analysis unit 13 determines, for example, a member network in which all betweenness centralities are 0 and the number of complete graphs is 0 as a "distributed type". In addition, the network feature analysis unit 13 determines, for example, a member network in which the top two betweenness centralities are 0.2 or less and the number of complete graphs is 2 or more as a "silo type". In addition, the network feature analysis unit 13 determines, for example, a member network in which the top two betweenness centralities are 0.9 or more and the number of complete graphs is 0 as a "bridge type". In addition, the network feature analysis unit 13 determines, for example, a member network in which the number of complete graphs is 0 and the average shortest distance is 5 to 6 as a "bonding type". Note that these values ​​are merely examples and can be set arbitrarily. The device 10 may store in advance table data showing the relationship between the feature amounts and each of the network types as shown in FIG. 6, or may obtain the data from outside via the communication line network.

[0041] Then, the organizational state identification unit 14 of the device 10 identifies the state of an organization having the decentralized member network as a "formation stage," the state of an organization having the silo-type member network as a "confusion stage," the state of an organization having the bridge-type member network as a "unification stage," and the state of an organization having the bonding-type member network as a "functional stage."

[0042] An example of betweenness centrality calculation will be described with reference to FIG. 7. The betweenness centrality can be calculated, for example, using formula (1) shown in FIG. 7. Specifically, an explanation will be given using node 1 shown in the lower part of FIG. 7 as an example. The betweenness centrality can be calculated by dividing the number of paths (B) that pass through node 1 among the shortest paths by the number of shortest paths (A) among the paths between each node, and finding the sum of the results. In other words, the betweenness centrality of node 1 shown in the lower part of FIG. 7 is 3.5.

[0043] An example of the calculation of the number of complete graphs will be described with reference to FIG. 8. First, n arbitrary nodes (n is an integer equal to or greater than 1) are extracted from the member network. Then, if the number of edges m (m is an integer equal to or greater than 1) consisting of the extracted n nodes is the same as the number of extracted nodes, the member network portion is regarded as a complete graph. The sum of the complete graphs is regarded as the number of complete graphs. Specifically, in the member network (1) shown in the lower part of FIG. 8, each node enclosed in a frame is extracted. In this case, as shown in the figure, the portion of the member network within each frame is a complete graph (n=3, m=3). On the other hand, in the member network (2), each node enclosed in a frame is extracted. In this case, as shown in the figure, the portion of the member network within each frame is not a complete graph (n=2, m=1).

[0044] An example of the calculation of the average shortest distance will be described with reference to FIG. 9. First, any one node is selected from the member network. Hereinafter, the selected node is also referred to as the origin. Next, as shown in the figure, a node adjacent to the origin is set to have an Erdős number n. Also, a node that is not adjacent to the origin and has an Erdős number n (n is an integer equal to or greater than 1) is set to have an Erdős number n+1. On the other hand, a node that does not meet the above criteria is set to have an Erdős number ∞. Then, the average shortest distance can be calculated, for example, using formula (2) shown in FIG. 9.

[0045] Next, an example of matching a coach with a team will be described. The coach, for example, uses a website (questionnaire) to input skill data. Then, the device 10 acquires the skill data, for example, by the skill data acquisition unit 15. FIG. 4(B) shows an example of the skill data. In FIG. 4(B), "Coach" indicates the identification information (name) of the coach, and "Skill" indicates the skill data of the coach (the state of the organization in which he excels).

[0046] Then, as shown in FIG. 10, the matching unit 16 matches the coach with the team. Specifically, as shown in FIG. 10(A), when the organizational state of the team is in the "unification period", the matching unit 16 matches the coach CC whose skill data, i.e., the organizational state of the coach who excels at coaching, is in the "unification period" to the team. In this way, the device 10 can match a suitable coach to the team. The device 10 may, for example, determine whether or not the combination of the current coach and the team is an appropriate combination. Specifically, as shown in FIG. 10(B), when the organizational state of the team is in the "formation period" and the skill data of the coach coaching the team, i.e., the organizational state of the coach who excels at coaching, is in the "unification period", the matching unit 16 may determine that the combination of the coach and the team is inappropriate (mismatch). Furthermore, when the matching unit 16 determines that the combination is inappropriate, for example, the matching unit 16 may select and output a recommended coach, i.e., a coach who has appropriate skill data. In the example shown in Fig. 10(B), instead of coach CC, coach AA, whose organizational state of expertise is "formative", is selected as the recommended coach. The format of the output is not particularly limited, and may be output to an external device via the communication line network, or may be output to be displayed on the display device 106, for example.

[0047] [Embodiment 3] The transmission of the thank-you data from one member to another member will be specifically described. Note that this is an example and is not limited in any way. The thank-you data can be transmitted from one member to another member by using, for example, the technology described in JP 2018-147494 A (hereinafter, also referred to as a thank-you transmission device).

[0048] The transmission of the thank you data is executed by, for example, a thank you transmission device 20. FIG. 11 shows an example of the configuration of the thank you transmission device 20 of the embodiment. The thank you transmission device 20 includes a memory unit 21, a display unit 22, a selection unit 23, and a transmission / reception unit 24. Furthermore, the thank you transmission device 20 may further include a statistics unit 25, etc., as an optional configuration. The hardware configuration of the thank you transmission device 20 is not particularly limited, but is the same as that of the tissue state identification device 10, for example. Specifically, the central processing unit 101 functions as, for example, the selection unit 23, the transmission / reception unit 24, and the statistics unit 25, the display unit 22 can use, for example, a display device 106, and the memory unit 21 can use the memory 102 or the storage device 104.

[0049] The gratitude transmission device 20 is a device owned by each user (i.e., member), and each gratitude transmission device 20 owned by each user may be connectable to each other, for example, via the communication line network.

[0050] Next, the thanks transmitting method of this embodiment will be described with reference to Figures 12 to 18. Figure 12 is a flowchart of the thanks transmitting method, and Figures 13 to 18 are schematic diagrams of display unit 22 of thanks transmitting device 20. Note that the thanks transmitting method of this embodiment is not limited to use of thanks transmitting device 20.

[0051] (A1) Memory process First, the storage unit 21 stores the thanks data and the user data of a plurality of users. The thanks data is as described above. The user data includes, for example, the terminal data of the user, and further includes, for example, identification data for identifying the user (also referred to as specific data for identifying the user). The storage unit 21 stores, as the user data, for example, the identification data of the user and the terminal data in association with each other. The user identification data may be, for example, information that can identify a specific user from a plurality of users, and specific examples thereof include a name, a nickname, a photographic image, an illustrated image, and the like, and may further include an affiliation, etc. The terminal data is, for example, data for transmitting and receiving data via the communication line network, and specific examples thereof include an IP address, etc. The number of user data stored in the storage unit 21 is not particularly limited, and for example, members belonging to any organization that conveys thanks can be stored. The plurality of users can be further stored, for example, as a group that invites members to share. The user data of the plurality of users may also be stored, for example, as an address book.

[0052] (A2) Display process of user data Next, the user identification information of the multiple users is displayed by the display unit 22. Specifically, the display unit 22 displays, for example, names, photographic images, illustration images, etc., as the user identification data for the multiple users. Specifically, as shown in Fig. 13, the display unit 22 of the gratitude transmission device 20 displays user names (e.g., Mr. A, Mr. B, etc.) and photographic images (e.g., face photos) or illustration images (e.g., illustrations of turtles), etc.

[0053] (A3) Selection process for a specific user Next, a specific user is selected from the multiple pieces of user identification information displayed by the selection unit 23. When a specific user is selected, as shown in the left diagram of Fig. 14, for example, the specific user (Mr. B) selected as the recipient of the thank-you message is displayed on the display unit 22.

[0054] (A4) Display process for multiple types of thanks Furthermore, when a specific user is selected by the selection unit 23, a plurality of types of thanks are displayed on the display unit 22 as the gratitude data in addition to the selected specific user. The types of thanks are not particularly limited, and examples include "thank you," "sorry," "praise," "respect," and "happy." The types of thanks are displayed, for example, as images expressing the types of thanks, and the images may be, for example, only words, only illustrations expressing gratitude, or a combination of words and illustrations.

[0055] The display unit 22 can display a plurality of types of thanks simultaneously or sequentially. In the latter case, for example, the display unit 22 can sequentially display the images indicating the plurality of types of thanks on the display unit 22 by moving an arbitrary image indicating the type of thanks displayed on the display unit 22 on the display unit 22 by sliding, swiping, or the like. At this time, in order to indicate that an image indicating the type of thanks exists in addition to the arbitrary image displayed on the display unit 22, the display unit 22 may display an arbitrary image indicating the type of thanks and a part of another adjacent image indicating the type of thanks to the left and right or above and below the arbitrary image. Specifically, as shown in the left diagram of FIG. 14, the display unit 22 displays a selected specific user (Mr. B), and further displays, as a type of thanks, the word "Good!", which is a word that evaluates the other person's behavior, and an image of an illustration representing it in the center of the downward direction. At this time, as shown in the left diagram of FIG. 14, a part of the other adjacent images is displayed to the left and right of the image of "Good!". This allows the user to recognize that another image exists to the left and right of the image of "Good!". By sliding the "Good!" image, for example, as shown in the right diagram of FIG. 14, the "Thank you" image to the right of the "Good!" image can be displayed in the center downward on the display unit 22.

[0056] (A5) Selection process of specific types of gratitude Next, a specific type of thanks is selected from the displayed types of thanks by the selection unit 23. When the specific type of thanks is selected, as shown in Fig. 15, for example, an image showing the selected specific user (Mr. B) and the selected type of thanks (Thank you) is displayed on the display unit 22.

[0057] (A6) Process for displaying multiple degrees of gratitude Next, when a specific type of thanks is selected by the selection unit 23, the display unit 22 displays, for example, the degree of thanks in addition to the selected type of thanks. The degree of thanks can be displayed, for example, by a bar image indicating the degree of thanks. Specifically, as shown in FIG. 15, a five-level bar indicating the degree of thanks is displayed next to an image indicating the type of thanks (thank you). The bar indicates, for example, the degree of thanks by its height, and the higher the bar is, the stronger the degree of thanks.

[0058] (A7) Selection process of specific degree of gratitude Next, the selection unit 23 selects a specific degree of gratitude from the degrees of gratitude displayed on the display unit 22. When a specific degree of gratitude is selected, a bar showing the degree of gratitude on a five-level scale is displayed on the display unit 22, for example, as shown in FIG. 15. For example, as shown in the left diagram of FIG. 15, when the degree of gratitude is displayed as three levels as a provisional standard (the bars from the bottom to the third level are displayed in black), as shown in the right diagram of FIG. 15, the color of the bar up to the fifth level can be made black to show that the degree of gratitude to B is five levels in a five-level evaluation. The color of the bar is not particularly limited, and, for example, an evaluation level can be selected by swiping from the first level bar to the desired level bar among the five-level bars displayed on the display unit 22.

[0059] (A8) Transmission process Then, the transmission / reception unit 24 transmits the selected specific thank you data (for example, the specified type of thanks and the degree of thanks) to the terminal of the selected specific user. Specifically, as shown in the order of Figs. 16(A) to (C), the "Thank you" image is moved by flicking or dragging toward the image showing the selected specific user (Mr. B) displayed on the display unit 22. This allows the thank you data, in which the type of thanks is "Thank you" and the degree of thanks is "on a five-level scale," to be transmitted to the specific user, Mr. B, via the network to the terminal of the specific user.

[0060] (A9) Receiving step (not shown) The method of the present embodiment includes, for example, a step of receiving thank-you data (for example, a selected specific type of thank-you and a selected specific degree of thank-you) transmitted from another user. The step can be executed, for example, by the transceiver unit 24 and the display unit 22 of the thank-you transmission device 20. The timing of receiving the thank-you data is not particularly limited.

[0061] In this embodiment, the order in which the thank you data and the user identification information of the multiple users are displayed on the display unit 22 is not particularly limited; for example, as described above, the thank you data may be displayed after the user identification information is displayed, or both may be displayed simultaneously.

[0062] The thanks transmission method of this embodiment may further include, for example, a storage step of storing the transmitted thanks data and the received thanks data as transmission / reception history data, and can be executed, for example, by the memory unit 21 of the thanks transmission device 20.

[0063] In this embodiment, the display unit 22 may further display, for example, a list of part or all of the thank-you data transmitted from the terminal, and a list of part or all of the thank-you data received by the terminal. As shown in FIG. 17, for example, when a transmission list is selected on the display unit 22 of the terminal 20 that is a thank-you transmission device, a list of thank-you data transmitted from the terminal 20 can be displayed. In the list, the contents of the thank-you data displayed are, for example, the destination user, the type of thank-you, the degree of thank-you, the transmission time, etc. Also, although not shown, for example, when a reception list is selected on the display unit 22 of the terminal 20 that is a thank-you transmission device, a list of thank-you data received by the terminal 20 can be displayed. In the list, the contents of the thank-you data displayed are, for example, the type of thank-you, the degree of thank-you, the transmission time, etc., and the sender user may or may not be displayed, and may not be displayed as anonymity for ease of expressing gratitude.

[0064] (A10) Statistical process (not shown) In this embodiment, the thank-you transmission device 20 may further include, for example, a statistical unit 25, which calculates statistical data based on the transmitted thank-you data and the received thank-you data. In this case, the display unit 22 may further display, for example, the statistical data.

[0065] The types of statistical data based on the transmitted and received thank-you data by the statistical unit 25 are not particularly limited, and specific examples include the total amount of thank-you data transmitted from the terminal, the total amount of thank-you data received by the terminal, the ratio (ratio) between the amount of thank-you data transmitted and the amount of thank-you data received by the terminal, the transition of the amount of thank-you data over time, the ratio of types of thank-yous and the ratio of degrees of thank-yous in the transmitted and received thank-you data, etc. In addition, if each terminal (thank-you transmission device 20) can be connected to a server as described above, for example, the total amount of thank-you data of each terminal (thank-you transmission device 20) stored in the server can also be displayed as statistical data. Here, the amount of thank-you data is not particularly limited, and can be expressed, for example, as the product of the number (number of times) of transmitted and received thank-you data and the degree of thank-yous.

[0066] The statistical data, for example, as shown in Figure 18, can be displayed on the display unit 22 of the terminal 20, which is a gratitude transmission device, in the form of numerical values, pie charts, bar graphs, line graphs, etc., and can include the total amount of gratitude from multiple terminals connected via the communication network, the amount of gratitude sent and received by the terminals, the ratio between the amount of gratitude sent and the amount of gratitude received (for example, over the past six months), the ratio by level of gratitude, and trends in the amount of gratitude.

[0067] According to this embodiment, for example, the type of gratitude or the degree of gratitude can be easily conveyed to the other party. Specifically, for example, even when it is embarrassing to convey directly, when it is difficult to convey in front of a large group, when the other party is far away, when the timing to convey is missed, etc., the device and method of this embodiment can be used to easily convey gratitude. Therefore, by using the device and method of this embodiment in an organization in a company, for example, it can increase mutual trust, and as a result, it can be useful for improving the performance of individuals and the organization. Therefore, the gratitude data and statistical information in this embodiment are useful as gratitude data transmission history information in the device and method described in the first and second embodiments.

[0068] [Embodiment 4] The program of the present embodiment is a program for causing a computer to execute each step of the method of each of the above-mentioned embodiments as a procedure. In the present invention, the "procedure" may be read as "processing." The program of the present embodiment may be recorded, for example, in a computer-readable recording medium. The recording medium is not particularly limited, and examples thereof include a read-only memory (ROM), a hard disk (HD), and an optical disk.

[0069] Although the present invention has been described above with reference to the embodiment, the present invention is not limited to the above embodiment. 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. [Industrial Applicability]

[0070] According to the present invention, the state of an organization can be grasped. Therefore, the present invention is useful, for example, in various fields having an organization formed by a plurality of members. [Explanation of symbols]

[0071] 10. Tissue condition identification device 11 Gratitude Data Acquisition Department 12 Network Generation Section 13 Network Feature Analysis Unit 14 Organizational Status Identification Section 15 Skill Data Acquisition Department 16 Matching section 20 Gratitude Transmission Device 21 Memory section 22 Display section 23 Selection section 24 Transmitter / receiver 25 Statistics Department 101 Central Processing Unit 102 Memory 103 Bus 104 Storage device 105 Input Device 106 Display device 107 Communication Devices

Claims

1. The system includes a gratitude data acquisition unit, a network generation unit, a network feature analysis unit, and an organization state identification unit, the thank you data acquisition unit acquires thank you data transmission history information indicating that thank you data has been transmitted from one member to another member; The network generation unit generates a member network within an organization to which the member belongs based on the gratitude data transmission history information, the network feature analysis unit analyzes at least one index selected from the group consisting of betweenness centrality, the number of complete graphs, and an average shortest distance in the member network as a feature of the member network; The organizational state identification unit identifies an organizational state based on the feature amount of the analyzed member network, Further, the device includes a skill data acquisition unit and a matching unit, The skill data acquisition unit acquires skill data for each instructor, The skill data includes information on a type of organizational state that the instructor is good at teaching; The matching unit is an organization state identification device that matches the organization with an instructor who will instruct the organization from among the multiple instructors based on the skill data and the organization state.

2. The network feature analysis unit further determines a network type of the member network from the feature, The network type includes at least one selected from the group consisting of a distributed type, a silo type, a bridge type, and a bonding type; The organizational state identification unit identifies an organizational state based on the network type; 2. The tissue state identification device according to claim 1, wherein the tissue state is at least one selected from the group consisting of a formation stage, a disorganization stage, a consolidation stage, a functioning stage, and a dispersion stage.

3. The method includes a gratitude data acquisition step, a network generation step, a network feature analysis step, and an organizational state identification step. The thank you data acquisition step acquires thank you data transmission history information indicating that thank you data has been transmitted from one member to another member, The network generating step generates a member network within an organization to which the member belongs based on the gratitude data transmission history information, The network feature analysis step includes analyzing at least one index selected from the group consisting of betweenness centrality, the number of complete graphs, and an average shortest distance in the member network as a feature of the member network; The organizational state identification step includes identifying an organizational state based on the feature amount of the analyzed member network, Further, the method includes a skill data acquisition step and a matching step, The skill data acquisition step acquires skill data for each instructor, The skill data includes information on a type of organizational state that the instructor is good at teaching; The matching step is a method for identifying an organizational state, the matching step including matching the organization with an instructor who will instruct the organization from among a plurality of instructors, based on the skill data and the organizational state.

4. The network feature analysis step further includes determining a network type of the member network from the feature, The network type includes at least one selected from the group consisting of a distributed type, a silo type, a bridge type, and a bonding type; The organizational state identifying step identifies an organizational state based on the network type; 4. The method of claim 3, wherein the tissue state is at least one selected from the group consisting of a formation stage, a disorganization stage, an integration stage, a functioning stage, and a dispersion stage.

5. A program for causing a computer to execute procedures including a gratitude data acquisition procedure, a network generation procedure, a network feature analysis procedure, and an organization state identification procedure, The thank you data acquisition step acquires thank you data transmission history information indicating that thank you data has been transmitted from one member to another member; The network generation step generates a member network within an organization to which the member belongs based on the gratitude data transmission history information; The network feature analysis step includes analyzing at least one index selected from a group consisting of betweenness centrality, the number of complete graphs, and an average shortest distance in the member network as a feature of the member network; The organizational state identification step includes identifying an organizational state based on the feature amount of the analyzed member network; Further, the method includes a skill data acquisition step and a matching step, The skill data acquisition step acquires skill data for each instructor, The skill data includes information on a type of organizational state that the instructor is good at teaching; The matching procedure is a program for matching the organization with an instructor who will instruct the organization from among a plurality of instructors, based on the skill data and the organizational state.

6. The network feature analysis step further includes determining a network type of the member network from the feature; The network type includes at least one selected from the group consisting of a distributed type, a silo type, a bridge type, and a bonding type; the organizational state identification step identifies an organizational state based on the network type; The program according to claim 5 , wherein the tissue state is at least one selected from the group consisting of a formation stage, a disorganization stage, a consolidation stage, a functioning stage, and a dispersion stage.

7. 7. A computer-readable recording medium having the program according to claim 5 or 6 recorded thereon.

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