Information processing device, information processing method, and information processing program

Abstract

This information processing device calculates a motivation value F0 of each worker on the basis of a subjective evaluation (expectation E, valence V, tool properties I) regarding duties before workers starts their duties. After the workers have started their duties, the information processing device estimates the subjective evaluation (expectation E, valence V, tool properties I) regarding the duties again on the basis of a communication log and a questionnaire result for each worker and calculates a motivation value F on the basis of the result of estimation. When it is determined by the information processing device that there is a worker whose motivation value F has decreased by a prescribed value or more from the motivation value F0, the workers are rearranged so as to optimize an evaluation value including the motivation value of each worker.

Description

Information processing device, information processing method, and information processing program

[0001] The present invention relates to an information processing device, an information processing method, and an information processing program for optimally allocating workers to each task.

[0002] A technology has been proposed that optimally allocates workers to each task based on the skills and costs of each worker.

[0003] Japanese Patent Application Laid-Open No. 2017-211921

[0004] Talent Palette automatic placement suggestion function, [online], [searched June 16, 2023], Internet <https: / / www.talent-palette.com / work / 03 / > "Hidden illness" detection by wellday and Kitasato University, [online], [searched June 16, 2023], Internet <https: / / prtimes.jp / main / html / rd / p / 000000020.000047158.html>

[0005] However, even if each worker is assigned to each task based on their skill and cost, there are cases where the task they are assigned to does not motivate them. Therefore, it is important to optimally assign each worker to each task, taking into account each worker's motivation.

[0006] However, in the past, there was no technology for evaluating the motivation of each worker for each task as described above, and therefore it was not possible to optimally allocate workers to each task, including the motivation of each worker. Therefore, an object of the present invention is to optimally allocate workers to each task, including the motivation of each worker.

[0007] In order to solve the above-mentioned problems, the present invention is characterized by comprising an estimation unit that estimates, for each worker, a first evaluation value which is a subjective evaluation value of the likelihood that the worker will succeed in the task, a second evaluation value which is a subjective evaluation value of the attractiveness of the secondary effects that will result from the performance of the task, and a third evaluation value which is a subjective evaluation value of the likelihood that the performance of the task will result in a secondary effect, based on the tasks of each worker and the analysis results of the communication log and questionnaire results for the tasks of each worker; and an allocation calculation unit that calculates a motivation value for each worker when the worker performs each task based on the estimated first evaluation value, second evaluation value, and third evaluation value of each worker, calculates an allocation of workers for each task that optimizes the evaluation value including the motivation value of each worker based on the calculated motivation value for each worker when the worker performs each task, and outputs the calculated allocation of workers for each task.

[0008] According to the present invention, it is possible to optimally allocate workers to each task, taking into account the motivation of each worker.

[0009] FIG. 1 is a diagram for explaining an overview of an information processing device. FIG. 2 is a diagram showing an example of the configuration of an information processing device. FIG. 3 is a diagram showing examples of evaluation values ​​of expectation E, volition V, and instrumentality I estimated by the information processing device. FIG. 4 is a diagram for explaining an example of estimation of the evaluation value of expectation E. FIG. 5 is a diagram showing an example of the motivation requirement DB of FIG. 2. FIG. 6 is a flowchart showing an example of a processing procedure executed by the information processing device. FIG. 7 is a diagram showing a computer that executes an information processing program.

[0010] Hereinafter, a description will be given of an embodiment of the present invention with reference to the drawings. The present invention is not limited to this embodiment. In the following description, an example will be described in which the information processing device uses Bloom's expectancy theory when estimating a worker's motivation value for work, but the present invention is not limited to this.

[0011] [Overview] First, an overview of the information processing device of this embodiment will be described. The information processing device calculates the motivation value for each worker's work using information indicating the personality of each worker (person information), the results of a questionnaire about each worker's work, etc. Then, the information processing device calculates an arrangement of workers that maximizes the evaluation value including the motivation value of each worker. For example, the information processing device calculates an arrangement of workers that maximizes the sum of the above evaluation values ​​of each worker.

[0012] For example, the information processing device calculates the motivation value F0 of each worker before the start of work, and calculates a worker allocation that maximizes the evaluation score including the motivation value F0. The information processing device then calculates the motivation value F of each worker after the start of work in the above allocation using personal information, questionnaire results, etc. of each worker. If the information processing device determines that there is a worker whose motivation value F after the start of work has decreased by more than a predetermined value compared to the motivation value F0 before the start of work, it recalculates the worker allocation that maximizes the evaluation score including the motivation value F of each worker. The information processing device then outputs the recalculated worker allocation.

[0013] In this way, the information processing device can optimally allocate workers to each task, taking into consideration the motivation of the workers.

[0014] [Processing Overview] Next, an overview of the processing executed by the information processing device will be described with reference to Fig. 1. First, the DBs (databases) included in the information processing device will be described. The information processing device includes, for example, a work DB, a skill DB, a business requirement DB, a people information DB, a questionnaire result DB, a motivation requirement DB, etc.

[0015] The task DB is a DB that stores information indicating the tasks of each worker. The skill DB is a DB that stores information indicating the skills possessed by each worker and the level of those skills. The business requirement DB is a DB that stores information indicating the skills required for each task and the level of those skills for that task. The human information DB is a DB that stores human information about each worker. The survey result DB is a DB that stores the results of a survey regarding the tasks of each worker. The motivation requirement DB is a DB that stores information indicating the secondary effects obtained by performing each task.

[0016] [Processing before work begins] First, the processing before each worker starts work will be explained. The information processing device refers to various databases to estimate the evaluation values ​​of the three elements used in Bloom's expectancy theory (expectation E, valence V, and instrumentality I; details will be given later). Note that the information processing device estimates the evaluation value of expectation E before each worker starts work by referring to the skill database and the job requirements database, but if the subjectivity of the worker is to be taken into account, it also refers to the questionnaire result database.

[0017] The information processing device then calculates the motivation value F0 of each worker before the start of the task based on the estimated evaluation values ​​of each worker's expectation E, volition V, and instrumentality I. The information processing device calculates an allocation that maximizes the evaluation values ​​including the motivation value F0 of each worker. The motivation value F0 of each worker is stored in a memory unit.

[0018] [Processing after the start of work] After that, when each worker starts their work in the above-mentioned arrangement, the information processing device, for example, performs text mining of each worker's communication log and measures the attractiveness (attractiveness V) of the secondary effects of each worker's performance of their work based on the results of analyzing each worker's emotions.

[0019] The secondary effects of performing a task include, for example, acquiring skills through the performance of the task, receiving a high evaluation, gaining discretion, feeling a sense of cooperation with other workers, etc. The information processing device then updates the human information DB using the measurement results of the attractiveness (attractiveness V) of each secondary effect of each worker.

[0020] Furthermore, when the information processing device acquires the results of the questionnaire answered by each worker, the information processing device updates the questionnaire result DB using the results of the questionnaire. The questionnaire may be, for example, a questionnaire about instrumentality I, a questionnaire about expectation E, etc.

[0021] A questionnaire about instrumentality I is, for example, a questionnaire about the likelihood that completing a task will lead to secondary effects. A questionnaire about expectancy E is, for example, a questionnaire about the likelihood that a task will be successful.

[0022] The information processing device estimates the evaluation values ​​of each worker's expectation E, volition V, and instrumentality I based on each updated DB. Then, the information processing device calculates each worker's motivation value F while performing their work based on the estimated evaluation values ​​of each worker's expectation E, volition V, and instrumentality I.

[0023] If the information processing device determines that there is a worker whose motivation value F has decreased by more than a threshold value compared to the motivation value F0, it recalculates the allocation of each worker. For example, the information processing device calculates a combination of workers for each task that will maximize the evaluation value, including the motivation of each worker.

[0024] As a result, if there is a worker whose motivation has decreased, the information processing device can perform a reassignment so as to optimize the evaluation value including the motivation of each worker.

[0025] [Configuration Example] Next, a configuration example of the information processing device 10 will be described with reference to Fig. 2. The information processing device 10 includes, for example, an input / output unit 11, a storage unit 12, and a control unit 13.

[0026] The input / output unit 11 is an interface that controls the input and output of various data. The input / output unit 11 receives input of, for example, communication logs of each worker, questionnaire results for each worker, and the like.

[0027] The storage unit 12 stores data, programs, etc. that are referenced when the control unit 13 executes various processes. The storage unit 12 is realized by a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk.

[0028] The storage unit 12 stores, for example, a work DB, a skill DB, a business requirement DB, a people information DB, a survey result DB, a motivation requirement DB, a communication log, and the like.

[0029] The work DB is a DB that stores information indicating the work of each worker (see Figure 4). The skill DB is a DB that stores information indicating the skills and levels of each worker (see Figure 4). For example, the skill DB shown in Figure 4 indicates that worker A's IT skill level is "5," his coordination ability level is "3," and his management ability level is "1."

[0030] The job requirements database stores information indicating the skills required for each job and the level of those skills. For example, in the job requirements database shown in Figure 4, the skills required for job "1" are "IT skills," "adjustment skills," and "management skills," and the required levels for each skill are "5," "2," and "1."

[0031] The motivation requirement DB is a DB that stores information indicating the secondary effects (secondary results) obtained by performing each task. For example, the motivation requirement DB shown in Figure 5 indicates that the secondary effects obtained by performing task "1" are "skill acquisition" and "receiving high evaluation." It also indicates that the secondary effects obtained by performing task "2" are "skill acquisition" and "discretion."

[0032] Returning to the explanation of Figure 2, the human information DB is a DB that stores human information about each worker. The human information is information that indicates the preferences and inclinations of the worker. The human information includes, for example, an attractiveness value for each secondary effect of the worker's performance of their duties (e.g., being able to acquire skills, receiving high evaluations, gaining discretion, feeling a sense of cooperation with other workers, etc.).

[0033] The survey result DB is a DB that stores the results of a survey regarding the work of each worker. The survey results regarding work include, for example, the results of a survey regarding the likelihood that the performance of the work will lead to secondary effects (instrumentality I) and the results of a survey regarding the likelihood that the work will be successful (expectation E). The survey result DB is updated and updated as new survey results are administered to each worker.

[0034] The communication log is a log of communication between each worker. This communication log is, for example, a log of emails and chats sent by each worker. This communication log may be stored outside the information processing device 10. In this case, the information processing device 10 acquires the communication log via a network.

[0035] The control unit 13 is responsible for overall control of the information processing device 10. The functions of the control unit 13 are realized, for example, by a CPU (Central Processing Unit) executing a program stored in the storage unit 12. The control unit 13 includes, for example, a DB update unit 131, an estimation unit 134, a motivation calculation unit 138, a determination unit 139, and an allocation calculation unit 140.

[0036] (DB Update Unit) The DB update unit 131 includes a people information DB update unit 132 and a questionnaire result DB update unit 133 .

[0037] (Personal Information DB Update Unit) The personal information DB update unit 132 uses the communication log to update the personal information of each worker stored in the personal information DB.

[0038] For example, the human information DB update unit 132 uses the analysis results of the communication log to measure the attractiveness (attractiveness V) of each of the secondary effects obtained by the worker's performance of their duties (e.g., being able to acquire skills, receiving high evaluations, gaining discretion, feeling a sense of cooperation with other workers, etc.). The human information DB update unit 132 reflects the measurement results of the attractiveness V of the worker in the human information of the worker in the human information DB.

[0039] For example, consider a case where the human information DB update unit 132 measures the attractiveness of "acquiring system verification skills" to a certain worker from the communication log. In this case, the human information DB update unit 132 extracts logs containing words related to the keyword "verification" and logs in the vicinity thereof from the worker's communication log. Note that words related to the keyword can be estimated using a synonym estimation technique such as a thesaurus.

[0040] The human information DB update unit 132 then analyzes the worker's emotions from the extracted log. The more positive the person's emotions obtained from the emotion analysis, the higher the attractiveness value for "** Acquiring system verification skills."

[0041] Note that some secondary effects obtained by performing work are difficult to extract logs or analyze emotions using only one keyword and its related words, such as gaining discretion or a sense of cooperation with other workers. For such secondary effects, the human information DB update unit 132 may extract logs based on scenes or situations estimated from the amount of speech made by the person in the communication log, and analyze the emotions of the worker from the extracted logs.

[0042] (Survey Result DB Updater) The survey result DB updater 133 updates the survey results of each worker stored in the survey result DB using the results of the survey administered to each worker.

[0043] For example, the survey result DB update unit 133 updates the survey results on tooliness I of a worker stored in the survey result DB using the results of a survey on tooliness I administered to the worker. Also, the survey result DB update unit 133 updates the survey results on expectation E of a worker stored in the survey result DB using the results of a survey on expectation E administered to the worker.

[0044] (Estimation Unit) The estimation unit 134 refers to each DB and estimates, for each worker, a subjective evaluation value of the likelihood of completing the task (evaluation value of expectation E), a subjective evaluation value of the attractiveness of the secondary effect of completing the task (evaluation value of volition V), and a subjective evaluation value of the likelihood that completing the task will result in a secondary effect (evaluation value of instrumentality I). The estimation unit 134 includes an expectation estimation unit 135, an attractiveness estimation unit 136, and an instrumentality estimation unit 137.

[0045] (Expectation Estimation Unit) The expectation estimation unit 135 estimates an evaluation value of the expectation E for each worker. The expectation E is a subjective probability that a worker can accomplish his or her assigned task if he or she makes a certain effort.

[0046] For example, the expectation estimation unit 135 acquires the survey results on the worker's expectation E from the survey result DB. Then, the expectation estimation unit 135 estimates the evaluation value of the worker's expectation E based on the acquired survey results.

[0047] If there are no survey results for the worker's expectation E, the expectation estimation unit 135 may estimate the evaluation value of the expectation E as follows. For example, the expectation estimation unit 135 acquires the skills required for the worker's work and the level of those skills from the job requirement DB. The expectation estimation unit 135 also acquires the skills possessed by the worker and the level of those skills from the skill DB. Then, the expectation estimation unit 135 determines the difference between the level of the skills required for the work possessed by the worker and the level required for the work as the evaluation value of the expectation E.

[0048] In other words, the higher the skill level of the worker is compared to the skill level required for the worker's job, the more likely it is that the worker will be able to complete the job. Therefore, the expectation estimation unit 135 estimates the difference between the worker's skill level required for the job and the level required for the job as the evaluation value of expectation E.

[0049] For example, consider a case where the expectation estimation unit 135 estimates the evaluation value of expectation E for worker A shown in FIG. 4. In this case, first, the expectation estimation unit 135 acquires the levels of each skill possessed by worker A (IT skill level "5", adjustment ability level "3", and management ability level "1") from the skill DB. Next, the expectation estimation unit 135 refers to the task DB and identifies that worker A's task is "1". Thereafter, the expectation estimation unit 135 acquires the required levels of each skill required for task "1" (IT skill level "5", adjustment ability level "2", and management ability level "1") from the job requirement DB.

[0050] Then, the expectation estimation unit 135 calculates an evaluation value of the expectation E for each skill based on the difference between the worker's level and the required level. For example, as shown in the following formula (1), the evaluation value is a value obtained by correcting the difference in the level of each skill to a probability value.

[0051]

[0052] In this way, the expectation estimation unit 135 can estimate the evaluation value of the expectation E of the worker.

[0053] (Voluntariness Estimation Unit) The voluntariness estimation unit 136 estimates an evaluation value of the voluntariness V for each worker. The voluntariness V is a subjective evaluation of the attractiveness of each secondary effect (secondary result) obtained by performing a task.

[0054] For example, the volition estimation unit 136 acquires the measurement result of the volition V of the worker, which is included in the human information of the worker acquired from the human information DB, as the evaluation result of the volition V of the worker.

[0055] For example, if the measurement results of the worker's attractiveness V included in the worker's human information are "skill acquisition = 0.8," "receiving high evaluations = 0.6," "discretion = 0.2," and "sense of collaboration = -0.5," the attractiveness estimation unit 136 acquires the above measurement results of attractiveness V as the evaluation results of the worker's attractiveness V (see Figure 3).

[0056] (Toolness Estimation Unit) Returning to the explanation of Figure 2, the toolness estimation unit 137 estimates the evaluation value of toolness I for each worker. Toolness I is a subjective expectation that the performance of a task will result in a secondary effect (secondary result). The toolness estimation unit 137 acquires the results of a survey on toolness I for the worker from the survey result DB, and estimates the evaluation value of toolness I from the survey results.

[0057] For example, the tool property estimation unit 137 estimates the following evaluation values ​​for tool property I from the results of a questionnaire about the worker's tool property I: "skill acquisition = 0.9," "receiving high evaluations = 0.8," "discretion = 0.3," and "sense of collaboration = 0.4" (see FIG. 3).

[0058] In addition, after each worker starts their work, the estimation unit 134 re-estimates the evaluation values ​​of each worker's expectation E, volition V, and instrumentality I at predetermined intervals or when the human information DB or survey result DB is updated.

[0059] (Motivation Calculation Unit) The motivation calculation unit 138 calculates the motivation value F for the work of the worker based on the motivation requirement DB (see FIG. 5) and the evaluation values ​​(see FIG. 3) of the worker's expectation E, volition V, and instrumentality I. The motivation calculation unit 138 then stores the calculated motivation value F for the work of the worker in the storage unit 12.

[0060] For example, the motivation calculation unit 138 refers to the motivation requirement DB and identifies the secondary effects obtained by the worker's performance of the task. For example, if the task of worker A is "1," the motivation calculation unit 138 identifies the secondary effects of task "1" from the motivation requirement DB shown in FIG. 5 as "skill acquisition" and "receiving a high evaluation."

[0061] Next, the motivation calculation unit 138 obtains the evaluation values ​​of the expectation E, volition V, and instrumentality I for task "1" of worker A from the estimation unit 134. Then, the motivation calculation unit 138 substitutes the evaluation value of the expectation E for task "1" of worker A and the evaluation values ​​of the volition V and instrumentality I for "skill acquisition" and "receiving a high evaluation" into formula (2) to calculate the motivation value F.

[0062]

[0063] For example, the motivation calculation unit 138 substitutes the evaluation value of expectation E = 0.5, the evaluation value of volition V (skill acquisition = 0.8, high evaluation = 0.6), and the evaluation value of instrumentality I (skill acquisition = 0.9, high evaluation = 0.8) shown in Figure 3 into formula (2) to calculate the motivation value F for task "1" of worker A. For example, the motivation calculation unit 138 calculates the motivation value F for task "1" of worker A as follows:

[0064] Motivation score F = 0.5 x (0.8 x 0.9 x 1 + 0.6 x 0.8 x 1 + 0.2 x 0.3 x 0 + (-0.5) x 0.4 x 0)

[0065] Furthermore, when the estimation unit 134 re-estimates the evaluation values ​​of the expectation E, valence V, and instrumentality I of a worker, the motivation calculation unit 138 re-calculates the motivation value F of the worker using the re-estimated evaluation values ​​of the expectation E, valence V, and instrumentality I. This allows the motivation calculation unit 138 to calculate the latest motivation value F of each worker.

[0066] (Determination Unit) Returning to the explanation of Fig. 2, the determination unit 139 acquires the latest motivation value F of each worker, and determines whether there is any worker whose acquired motivation value F has decreased by a predetermined threshold or more compared to the previous motivation value.

[0067] For example, the determination unit 139 determines whether there is a worker whose motivation value F after starting a task has decreased by a predetermined threshold or more from the motivation value F0 before starting the task.

[0068] (Allocation Calculation Unit) The allocation calculation unit 140 calculates the allocation of workers for each task so as to optimize the evaluation value including each worker's motivation value F, and outputs the calculated allocation of workers for each task. Note that the above evaluation value is calculated taking into consideration, for example, each worker's skill, cost, etc. in addition to each worker's motivation value F.

[0069] For example, first, the allocation calculation unit 140 calculates the allocation of workers for each task so as to optimize the evaluation values ​​including the motivation value F of each worker. This allocation is set as the initial allocation. Then, after each worker starts working under the initial allocation, if the determination unit 139 determines that there is a worker whose motivation value F has decreased by more than a predetermined threshold, the allocation calculation unit 140 again calculates the allocation of workers for each task so as to optimize the evaluation values ​​including the motivation value F of each worker.

[0070] The allocation calculation unit 140 calculates the allocation of workers for each task, for example, as follows. First, the allocation calculation unit 140 calculates the motivation value F for each task of each worker. For example, the allocation calculation unit 140 acquires the evaluation values ​​of worker A's expectancy E, volition V, and instrumentality I estimated by the estimation unit 134 (see FIG. 3). The allocation calculation unit 140 also acquires the secondary effects of tasks 1, 2, 3, and 4 listed in the motivation requirement DB (see FIG. 5). Then, based on the acquired evaluation values ​​of worker A's expectancy E, volition V, and instrumentality I (see FIG. 3) and the secondary effects of tasks 1, 2, 3, and 4, the allocation calculation unit 140 calculates the motivation value when worker A is in charge of tasks 1, 2, 3, and 4 (see formula (3)).

[0071]

[0072] Similarly to the above, the allocation calculation unit 140 also calculates the motivation values ​​of the other workers when they are in charge of tasks 1, 2, 3, and 4.

[0073] Next, the allocation calculation unit 140 calculates the allocation of workers for each task (e.g., tasks 1, 2, 3, and 4) based on the motivation value for each worker, so as to optimize the evaluation value including the motivation value of each worker. For example, the allocation calculation unit 140 calculates the allocation of workers for each task so as to maximize the total evaluation value including the motivation value of each worker.

[0074] For example, the placement calculation unit 140 determines the tasks for each of workers A and B based on the motivation value F when worker A is in charge of tasks 1, 2, 3, and 4, and the motivation value F when worker B is in charge of tasks 1, 2, 3, and 4, so that the total evaluation value of workers A and B is high.

[0075] In this way, the allocation calculation unit 140 can output an allocation that optimizes the evaluation values ​​including the motivation values ​​of each worker.

[0076] [Example of Processing Procedure] Next, an example of processing procedure executed by the information processing device 10 will be described with reference to FIG. 6. First, the motivation calculation unit 138 of the information processing device 10 calculates the motivation value (initial motivation value F0) of each worker before the start of their work (S1). Then, the information processing device 10 calculates the worker placement based on each worker's motivation value F0. Thereafter, each worker performs their work in the above placement. Then, the information processing device 10 performs the following processes S2 to S6 while each worker is performing their work.

[0077] First, the DB update unit 131 updates each DB (S2). For example, the human information DB update unit 132 of the DB update unit 131 updates the human information of each worker stored in the human information DB using a communication log of each worker while they are performing their work. Furthermore, the survey result DB update unit 133 updates the survey results of each worker stored in the survey result DB using the results of a survey conducted for each worker while they are performing their work.

[0078] The information processing device 10 then calculates the motivation value F of each worker while they are performing the task (S3). For example, the estimation unit 134 refers to each updated DB and re-estimates the evaluation value of each worker's expectation E, volition V, and instrumentality I. The motivation calculation unit 138 then calculates the motivation value F for each worker's task based on the motivation requirement DB (see FIG. 5) and the evaluation values ​​of each worker's expectation E, volition V, and instrumentality I estimated by the estimation unit 134 (see FIG. 3).

[0079] Thereafter, the determination unit 139 determines whether there is any worker whose motivation value F has decreased by a predetermined threshold or more compared to the motivation value F0. For example, the determination unit 139 calculates the difference between the motivation value F and the motivation value F0 for each worker, and calculates the fluctuation ΔF=(FF O ) / F0 (S4). Then, the determination unit 139 determines whether ΔF<ΔF th (threshold value) (S5). th If it is determined that there is a worker for whom ΔF<ΔF (Yes in S5), the allocation calculation unit 140 calculates the allocation of each worker based on the motivation value F for each task of each worker (S6). Then, the allocation calculation unit 140 outputs the calculated allocation. On the other hand, if the determination unit 139 determines that ΔF<ΔF th If it is determined that there is no worker who satisfies the condition (No in S5), the process returns to S3.

[0080] By executing the above process, the information processing device 10 can output an arrangement that optimizes the evaluation values ​​including the motivation values ​​of each worker when the motivation value of any worker drops.

[0081] [System Configuration, etc.] The components of each unit shown in the figure are conceptual functional units and do not necessarily have to be physically configured as shown. In other words, the specific form of distribution and integration of each device is not limited to that shown, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc. Furthermore, all or any part of the processing functions performed by each device can be realized by a CPU and a program executed by the CPU, or can be realized as hardware using wired logic.

[0082] Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using a known method.In addition, the information including the processing procedures, control procedures, specific names, various data and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified.

[0083] [Program] The information processing device 10 can be implemented by installing a program (information processing program) as package software or online software on a desired computer. For example, by executing the program on an information processing device, the information processing device can function as the information processing device 10. The information processing device referred to here includes mobile communication terminals such as smartphones, mobile phones, and PHS (Personal Handyphone Systems), as well as terminals such as PDAs (Personal Digital Assistants).

[0084] 7 is a diagram showing an example of a computer that executes an information processing program. The computer 1000 includes, for example, a memory 1010 and a CPU 1020. The computer 1000 also includes a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, and a network interface 1070. These components are connected by a bus 1080.

[0085] The memory 1010 includes a read-only memory (ROM) 1011 and a random access memory (RAM) 1012. The ROM 1011 stores a boot program such as a basic input / output system (BIOS). The hard disk drive interface 1030 is connected to a hard disk drive 1090. The disk drive interface 1040 is connected to a disk drive 1100. A removable storage medium such as a magnetic disk or optical disk is inserted into the disk drive 1100. The serial port interface 1050 is connected to a mouse 1110 and a keyboard 1120, for example. The video adapter 1060 is connected to a display 1130, for example.

[0086] The hard disk drive 1090 stores, for example, an OS 1091, an application program 1092, a program module 1093, and program data 1094. That is, the programs that define the processes executed by the information processing device 10 are implemented as program modules 1093 in which computer-executable code is written. The program modules 1093 are stored, for example, in the hard disk drive 1090. For example, the program modules 1093 for executing processes similar to those of the functional configuration of the information processing device 10 are stored in the hard disk drive 1090. Note that the hard disk drive 1090 may be replaced with an SSD (Solid State Drive).

[0087] Data used in the processing of the above-described embodiment is stored as program data 1094, for example, in the memory 1010 or the hard disk drive 1090. The CPU 1020 then reads the program module 1093 or the program data 1094 stored in the memory 1010 or the hard disk drive 1090 into the RAM 1012 as necessary and executes them.

[0088] The program module 1093 and program data 1094 may not necessarily be stored in the hard disk drive 1090, but may also be stored in a removable storage medium and read by the CPU 1020 via the disk drive 1100 or the like. Alternatively, the program module 1093 and program data 1094 may be stored in another computer connected via a network (such as a local area network (LAN) or a wide area network (WAN)). The program module 1093 and program data 1094 may then be read by the CPU 1020 from the other computer via the network interface 1070.

[0089] REFERENCE SIGNS LIST 10 Information processing device 11 Input / output unit 12 Memory unit 13 Control unit 131 DB update unit 132 Person information DB update unit 133 Questionnaire result DB update unit 134 Estimation unit 135 Expectation estimation unit 136 Volition estimation unit 137 Tooliness estimation unit 138 Motivation calculation unit 139 Determination unit 140 Placement calculation unit

Claims

1. An information processing device comprising: an estimation unit that estimates, for each worker, a first evaluation value which is a subjective evaluation value of the likelihood that the worker will succeed in the task, a second evaluation value which is a subjective evaluation value of the attractiveness of the secondary effects of performing the task, and a third evaluation value which is a subjective evaluation value of the likelihood that the performance of the task will result in a secondary effect, based on the tasks of each worker and the analysis results of each worker's communication log and questionnaire results for the tasks; and an allocation calculation unit that calculates a motivation value for each worker when the worker performs each task based on the estimated first evaluation value, second evaluation value, and third evaluation value of each worker, calculates an allocation of workers for each task that optimizes the evaluation value including the motivation value of each worker based on the calculated motivation value for each worker when the worker performs each task, and outputs the calculated allocation of workers for each task.

2. The information processing device according to claim 1, further comprising: a motivation calculation unit that calculates the motivation value of each worker before the start of the work and the motivation value after the start of the work based on the first evaluation value, the second evaluation value, and the third evaluation value of each worker before the start of the work and before the start of the work; and a judgment unit that judges whether there is any worker whose motivation value after the start of the work has dropped by a predetermined threshold or more compared to the motivation value before the start of the work, wherein when the judgment unit judges that there is a worker whose motivation value has dropped by the predetermined threshold or more, the allocation calculation unit calculates an allocation of workers for each task so as to optimize the evaluation value including the motivation value of each worker, and outputs the calculated allocation of workers for each task.

3. The information processing device according to claim 2, characterized in that the judgment unit further judges whether there is a worker whose latest motivation value after starting the task is lower than the previous motivation value after starting the task by a predetermined threshold value or more.

4. The information processing device of claim 2, characterized in that the estimation unit, after each worker starts working, re-estimates the first evaluation value, the second evaluation value, and the third evaluation value of each worker based on the analysis of each worker's communication log and the results of a questionnaire survey on the work, and the motivation calculation unit re-calculates the latest motivation value of each worker based on the re-estimated first evaluation value, the second evaluation value, and the third evaluation value of each worker.

5. The information processing device of claim 1, further comprising: a first update unit that updates human information indicating a subjective evaluation value of the attractiveness of the secondary effects of performing the task of each worker based on the analysis results of the communication log of each worker after the task has started; and a second update unit that updates a questionnaire result regarding the task of each worker based on the questionnaire result regarding the task of each worker after the task has started, wherein the estimation unit re-estimates the first evaluation value, the second evaluation value, and the third evaluation value of each worker based on the updated human information and questionnaire results of each worker.

6. An information processing method executed by an information processing device, comprising the steps of: estimating, for each worker, a first evaluation value which is a subjective evaluation value of the likelihood that the worker will succeed in the task, a second evaluation value which is a subjective evaluation value of the attractiveness of secondary effects resulting from the performance of the task, and a third evaluation value which is a subjective evaluation value of the likelihood that the performance of the task will result in secondary effects, based on the tasks of each worker and the results of an analysis of the communication log and questionnaire results for the tasks of each worker; calculating, based on the estimated first evaluation value, second evaluation value, and third evaluation value of each worker, a motivation value when each worker performs each task, calculating an allocation of workers for each task that optimizes the evaluation value including the motivation value of each worker based on the calculated motivation value when each worker performs each task, and outputting the calculated allocation of workers for each task.

7. An information processing program for causing a computer to execute the steps of: estimating, for each worker, a first evaluation value which is a subjective evaluation value of the likelihood that the worker will succeed in the task, a second evaluation value which is a subjective evaluation value of the attractiveness of secondary effects resulting from the performance of the task, and a third evaluation value which is a subjective evaluation value of the likelihood that the performance of the task will result in secondary effects, based on the tasks of each worker and the analysis results of each worker's communication log and questionnaire results for the tasks; calculating, based on the estimated first evaluation value, second evaluation value, and third evaluation value of each worker, a motivation value when each worker performs each task, calculating an allocation of workers for each task that optimizes the evaluation value including the motivation value of each worker based on the calculated motivation value when each worker performs each task, and outputting the calculated allocation of workers for each task.