Talent matching system and talent matching method

Abstract

For example, the aim is to provide a talent matching system and method that can accurately understand an individual's personality and, for example, eliminate mismatches between individuals and organizations in advance. [Solution] A human resource matching system 100 that utilizes personality data obtained by answering questions with a swipe gesture, comprising a storage unit 104 that stores one side personality data, which is one side of the population of personality data, and the other side personality data, which is the other side of the population of personality data, and a matching generation unit 106 that matches the one side personality data and the other side personality data.

Description

Technical Field

[0001] The present invention relates to a human resource matching system and a human resource matching method that utilize personality obtained by, for example, a swiping operation of a respondent.

Background Art

[0002] In recruitment activities for fresh graduates in a so-called seller's market, the proportion of companies whose offers of employment are declined by fresh graduates is on the increase. Even if a company hires a fresh graduate, the number of fresh graduates who leave the company in the first year is also increasing year by year. In such a situation, while the cost for a company to acquire the human resources it needs in the fresh graduate market is increasing year by year, the number of fresh graduates who leave the company early after recruitment is increasing, making it extremely difficult for companies to recruit students, and the fresh graduate recruitment activity has become a major management issue for companies.

[0003] The main reason for students to leave the company early is a mismatch in human relationships within the company. Furthermore, the reason for the mismatch in human relationships is due to the inability to accurately grasp an individual's personality.

[0004] In the company's student recruitment activities as described above, avoiding a mismatch in human relationships in advance can continuously satisfy both the company side and the student side in the company's fresh graduate recruitment activities and after recruitment, reduce the early departure of students, and smoothly advance company management, so great expectations are placed on it.

[0005] These things are not limited to students and companies. They are problems that occur in all social life, such as the relationship between an individual as a working person and a company organization, the relationship between a student and a school, the relationship between various organizations, and further the relationship between an individual and his or her community.

[0006] Furthermore, it is also a problem lurking in the matching of people for the purpose of marriage hunting.

Prior Art Documents

Patent Documents

[0007] [Patent Document 1] Japanese Patent Publication No. 2019-168887 [Patent Document 2] Japanese Patent Publication No. 6055794 [Overview of the Initiative] [Problems that the invention aims to solve]

[0008] Therefore, the present invention aims to provide a human resource matching system and method that can solve the above problems by accurately understanding an individual's personality and, for example, resolving mismatches between individuals and organizations in advance. [Means for solving the problem]

[0009] The first invention is a talent matching system that utilizes personality data obtained by answering questions with a swipe gesture, A storage unit that stores one side of the personality data, which is the population of one side, and the other side of the personality data, which is the population of the other side, A matching generation unit that matches the personality data of one side with the personality data of the other side, It is preferable that the system is a talent matching system that utilizes personality data and has the following characteristics.

[0010] In this case, the system may include a one-side application that inputs the one-side personality data and a other-side application that inputs the other-side personality data, and the one-side personality data input from the one-side application and the other-side personality data input from the other-side application may be stored in a storage unit.

[0011] In this case, the matching generation unit may receive the input of the one-side personality data and the other-side personality data, generate matching data, and output it to the one-side application and the other-side application, respectively.

[0012] In this case, the matching generation unit may use the one-side personality data and the other-side personality data as input data, and use a machine learning-based predictive model, for example, to output a plurality of matching models as recommended personality data, using a plurality of conceivable matching cases as training data.

[0013] In this case, the matching generation unit may search for and output content related to personnel matching based on the recommended personality data.

[0014] The second invention is a method of matching personnel using personality data obtained by answering questions with a swipe gesture, A first step of storing one side personality data, which is one of the populations of the personality data, and the other side personality data, which is the other population of the personality data. A second step of matching the personality data of one side with the personality data of the other side, It is preferable that the method is a talent matching method that utilizes personality data.

[0015] In this case, the system may have a third step of storing the one-side personality data input from the one-side application and the other-side personality data input from the other-side application, using a one-side application that inputs the one-side personality data and a other-side application that inputs the other-side personality data.

[0016] In this case, the process may include a fourth step of receiving the one-side personality data and the other-side personality data as inputs to generate matching data, and a fifth step of outputting the matching data to the one-side application and the other-side application, respectively.

[0017] In this case, the process may include a sixth step in which the one-sided personality data and the other-sided personality data are used as input data, and multiple conceivable matching cases are used as training data, and multiple matching models are output as recommended personality data, for example, using a machine learning-developed predictive model.

[0018] In this case, there may be a seventh step of searching for and outputting content related to talent matching based on the recommended personality data.

[0019] The third invention is a talent matching program that utilizes personality data obtained by answering questions with a swipe gesture, The first step involves storing the personality data of one population, which is the one-side personality data, and the personality data of the other population, which is the other-side personality data. A second step of matching the personality data of one side with the personality data of the other side, It is preferable that the program is a talent matching program that utilizes personality data.

[0020] In this case, the system may have a third step of storing the one-side personality data input from the one-side application and the other-side personality data input from the other-side application, using a one-side application that inputs the one-side personality data and a other-side application that inputs the other-side personality data.

[0021] In this case, it may include a fourth step of receiving the input of the one-sided personality data and the other-sided personality data to generate matching data, and a fifth step of outputting the matching data to the one-sided application and the other-sided application respectively.

[0022] In this case, it may include a sixth step of using the one-sided personality data and the other-sided personality data as input data, using a plurality of assumable matching cases as teacher data, and outputting a plurality of matching models as recommended personality data by using, for example, a machine-learned prediction model.

[0023] In this case, it may include a seventh step of searching for and outputting content related to talent matching based on the recommended personality data.

[0024] Note that as combinations of "one-sided" and "local side", for example, variations such as "individual side" and "individual side", "individual side" and "organization side", "organization side" and "organization side" are established as means for solving the problems of the present invention.

Advantages of the Invention

[0025] According to the present invention, each person's personality can be accurately grasped, and thus, for example, mismatches between individuals and organizations can be eliminated in advance.

Brief Description of the Drawings

[0026] [Figure 1] It is a block diagram of a personality diagnosis system according to a first embodiment of the present invention. [Figure 2] It is a diagram for explaining steps constituting a personality diagnosis method according to a first embodiment of the present invention. [Figure 3] It is a diagram for explaining correction of a swipe time obtained by a personality diagnosis system according to a first embodiment of the present invention. [Figure 4]This figure shows a scheme for personality diagnosis by swiping according to the first embodiment of the present invention. [Figure 5] This is a flowchart of a swipe diagnostic test according to the first embodiment of the present invention. [Figure 6] This figure compares the characteristics of the personality data obtainable in a conventional personality assessment and in the personality assessment of the first embodiment. [Figure 7] This figure illustrates the information (elements) used to determine personality in the personality measurement method according to the first embodiment of the present invention. [Figure 8] This diagram illustrates the relationship between coordinate movement on the device during a swipe gesture and the response intensity. [Figure 9] This is a conceptual diagram illustrating an example of the logic behind the response filter. [Figure 10] This is an explanatory diagram illustrating an example of the logic behind the response filter. [Figure 11] This figure shows an example of the filter strength detected when the logic is equipped with an autonomous learning function that allows it to learn the situation during the exam on its own. [Figure 12] This figure shows an example of averaged personality data. [Figure 13] This diagram shows various types of data stored in the database. [Figure 14] This figure shows the initial values ​​for tuning the response filter parameters and the expected continuous response trend. [Figure 15] This is a block diagram of a human resource matching system according to a second embodiment of the present invention. [Figure 16] This figure illustrates the steps comprising the personnel matching method according to the second embodiment of the present invention. [Figure 17] This figure shows a scheme of a human resource matching system according to a second embodiment of the present invention. [Figure 18] This is a flowchart of the personnel matching process according to the second embodiment of the present invention. [Figure 19]This figure shows an example of a machine learning scheme (recommended personality output) in a talent matching system according to the second embodiment of the present invention. [Figure 20] This figure shows an example of a machine learning scheme (matching target variable output) in a talent matching system according to the second embodiment of the present invention. [Figure 21] This diagram illustrates the relationship between a general optimization model and an organizational optimization model for achieving the optimal match. [Figure 22] This diagram shows an example of the logic used to define the optimal match. [Figure 23] This is a conceptual diagram illustrating an example of a matching algorithm between individuals and organizations. [Modes for carrying out the invention]

[0027] [First Embodiment] First, the personality measurement system, personality diagnosis system, personality measurement method, personality diagnosis method, and personality diagnosis program according to the first embodiment of the present invention will be described with reference to the drawings. The personality measurement system and personality diagnosis system may be installed or downloaded to an existing housing (for example, hardware such as a mobile phone, smartphone, or personal computer terminal or device), such as software such as application software, or they may be implemented using the hardware such as a terminal or device itself (for example, a personal computer or server).

[0028] More specifically, the personality measurement system, personality diagnosis system, personality measurement method, personality diagnosis method, and personality diagnosis program according to the first embodiment of the present invention are devices, processes, or programs for diagnosing the personality of a respondent, based on the premise that the respondent performs a swipe motion (also simply referred to as "swiping") on the screen of a terminal or device such as a smartphone.

[0029] Specifically, for example, the swipe time required for a swipe operation is the time it takes for the respondent to touch the touchscreen, slide their finger across the screen, and then lift their finger from the screen. While the respondent reads and understands the question, they do not perform a swipe operation to select an answer. After further understanding the question and considering their answer, they begin the swipe operation to select an answer. Once the swipe operation begins and is completed, the respondent's answer is determined, and the next question is displayed on the screen. The respondent then begins reading and understanding the next question, and then performs a swipe operation to select an answer for the next question. Therefore, the swipe time includes the time the respondent spends reading and understanding the question.

[0030] [1-1. Personality Assessment System] As shown in Figure 1, the personality diagnostic system 10 includes, for example, a time setting unit 12, a storage unit 14, a measurement unit 16, a determination unit 18, a discrimination unit 20, a generation unit 22, and a filter setting unit 24.

[0031] Here, the system comprising at least the measurement unit 16 and the judgment unit 18 of the personality diagnostic system 10 is referred to as the personality measurement system 11. The personality measurement system 11 is a device that can accurately measure the personality of a respondent through the respondent's swipe gesture. The personality measurement system 11 may also be further equipped with a discrimination unit 20.

[0032] In Figure 1, for the sake of explanation, an example is shown in which each part of the personality diagnostic system 10 is located on a single device or apparatus, but the system is not limited to this configuration. For example, a configuration in which the components are distributed across multiple different devices or apparatuses may be adopted. For example, a single device or apparatus may be solely for inputting swipe gestures, and the components may be located on a server or cloud outside the device or apparatus. Alternatively, a single device or apparatus may only contain, for example, the measurement unit 16, with the other components located on a server or cloud outside the device or apparatus. Furthermore, some of the components of the personality diagnostic system 10 may be composed of components such as a CPU, while the other components may be handled by software programs or other means that perform calculations.

[0033] Here, the time setting unit 12, measurement unit 16, determination unit 18, discrimination unit 20, generation unit 22, and filter setting unit 24 are composed of, for example, a central processing unit (CPU) or a control unit (controller).

[0034] The memory unit 14 consists of RAM (Random Access Memory), ROM (Read Only Memory), storage, etc. However, it is not limited to these configurations.

[0035] Furthermore, if the personality diagnostic system 10 is configured as software or a program such as application software, it may also mean, for example, steps or commands for driving the time setting unit 12, storage unit 14, measurement unit 16, determination unit 18, discrimination unit 20, generation unit 22, and filter setting unit 24, but is not limited to this.

[0036] The time setting unit 12 outputs a question based on question data stored in the memory unit beforehand and sets a standard response time for the respondent to answer the question. The standard response time set by the time setting unit 12 is stored in the memory unit 14. The timing of setting the standard response time by the time setting unit 12 can be freely changed, and it is possible to set it in many different patterns depending on the respondent's attributes (for example, age, gender, educational background, humanities, science, desired content, survey results, etc.).

[0037] The time setting unit 12 is a device or process for registering the respondent's personality data in a database.

[0038] The memory unit 14 stores various data and information necessary for personality assessment. For example, it can store the standard response time set by the time setting unit 12 and the content of the question at that time in association with it. The memory unit 14 also stores data related to the respondent's personal information (address, name, age, etc.) and attributes.

[0039] The memory unit 14 is a device or process for registering the respondent's personality data in a database.

[0040] The measurement unit 16 measures, for example, the time required for a respondent to perform a swipe motion on a touch panel screen as the swipe time. Specifically, the respondent answers questions displayed on the screen of the device or apparatus based on a swipe motion on the screen, and the measurement unit measures the time required for such a swipe motion. A swipe motion refers to the action of sliding a respondent's finger across a touch panel screen while the finger is in contact with the screen.

[0041] The time taken for a swipe refers to the time it takes for the respondent to touch the touchscreen, slide their finger across the screen, and then lift their finger from the screen. If multiple questions are provided, when a swipe is performed for the first question, the answer to the first question is selected, and the content of the second question is displayed on the screen. When a swipe is performed for the second question, the answer to the second question is selected, and the content of the third question is displayed on the screen. When a swipe is performed for the third question, the answer to the third question is selected, and the content of the fourth question is displayed on the screen. This cycle is repeated for the number of pre-set questions.

[0042] Furthermore, as a method for measuring the swipe time by the measurement unit 16, for example, the detection technology described in Japanese Patent No. 6055794 (see Patent Document 1 as prior art described in the specification) can be used or applied. Various data or information measured by the measurement unit 16 are appropriately stored in the storage unit 14.

[0043] The measurement unit 16 is a device or process for registering the respondent's personality data in a database.

[0044] The determination unit 18 determines the response intensity based on the measurement results from the measurement unit 16. For example, the storage unit 14 stores response intensity determination data or a response intensity determination table for determining the response intensity. The determination unit 18 may, for example, use the response intensity determination data or response intensity determination table to determine the response intensity based on the measurement results from the measurement unit 18. The response intensity determination data or response intensity determination table from the determination unit 18 may also be stored in the storage unit 14.

[0045] Regarding the determination of response strength, if the response time is short, it can be presumed that the respondent is answering based on their beliefs and confidence, and therefore the response strength is judged to be high. On the other hand, if the response time is long, it can be presumed that the respondent is answering while hesitating, and therefore the response strength is judged to be low. If a standard response time is set for each question by the time setting unit 12, the response strength may also be determined based on whether the response time is shorter or longer than the standard response time.

[0046] Even if a standard response time is not set, it is possible to determine response intensity based on, for example, the standard deviation from the typical response time of respondents.

[0047] Here, it is preferable for the determination unit 18 to determine the strength of the response based on the remaining time obtained by subtracting a predetermined time from the swipe time. This makes it possible to exclude the idle time it takes for the respondent to read and understand the question, and to measure the true time taken to answer, the strength of the response can be determined more accurately. For example, swipe time is the time it takes for the respondent to touch the touch panel screen, slide their finger across the screen, and then lift their finger from the screen. However, the respondent does not perform a swipe to select an answer while reading and understanding the question. Furthermore, after the respondent has understood the question and thought about an answer, they begin the swipe to select an answer. Once the respondent begins the swipe and completes the swipe, the respondent's answer is determined, and the next question is displayed on the screen. The respondent then begins reading the next question, understands it, and then performs a swipe to select an answer for the next question. For this reason, the swipe time includes the idle time the respondent spends reading and understanding the question, but this idle time is not an accurate indicator of the strength of the response. Therefore, we decided to exclude the playtime from the swipe time.

[0048] To illustrate this graphically, for example, as shown in Figure 2, if we define "T" as the "swipe time"—the time it takes for a respondent to touch a touchscreen, slide their finger across the screen, and then lift their finger—then the initial "S" is a predetermined amount of time (playtime) required to read and understand the question. Therefore, by correcting for this playtime ("S") and calculating the remaining time as "TS" (swipe time), a precise swipe time is determined to accurately assess the strength of the response.

[0049] Furthermore, in addition to the two response intensity patterns of "strong" and "weak," a "medium" pattern may also be included, and it is even possible to express responses in multiple patterns using multiple scales such as a 5-point scale, a 10-point scale, etc.

[0050] The determination unit 18 is a device or process for registering the respondent's personality data in a database.

[0051] The discrimination unit 20 determines the direction of the swipe motion performed by the respondent as the response direction. The response direction can also be called the swipe direction. The direction of the swipe motion means the swipe direction, and techniques for detecting the swipe direction itself have been known for some time. For example, it is possible to apply the techniques described in Japanese Patent Publication No. 2023-095529, Japanese Patent Publication No. 2023-172808, Japanese Patent Publication No. 2023-052046, Japanese Patent Publication No. 2022-158827, and Japanese Patent Publication No. 2020-039857.

[0052] Here, the direction of the respondent's swipe gesture, referred to as the "response direction," means the direction in which the respondent moves their finger across the device screen. However, the swipe direction may be predetermined for each question. For example, if a question requires a "yes" or "no" answer, the respondent can swipe to the right on the screen to select "yes," and swipe to the left on the screen to select "no." Also, for example, if a question requires a number such as "number 1" or "number 2," the respondent can swipe to the right on the screen to select "number 1," and swipe to the left on the screen to select "number 2."

[0053] While two response directions have been given as examples, the system is not limited to these. For example, it is possible to obtain a total of three to four response directions by adding at least one additional direction, such as up or down, in addition to the left and right directions. Furthermore, even more response directions can be obtained by swiping diagonally across the screen.

[0054] The discrimination unit 20 is a device or process for registering the respondent's personality data in a database.

[0055] The generation unit 22 generates personality data for the respondent from the response intensity determined by the determination unit 18 and the response direction determined by the discrimination unit 20. By constructing the personality data as "response intensity × response direction," it is possible to obtain personality data (big data) composed of patterns for many different respondents. The personality data generated by the generation unit 22 may be linked to the respondent and stored in the storage unit at a predetermined timing.

[0056] The generation unit 22 is a device or process for registering the respondent's personality data in a database.

[0057] The filter setting unit 24 sets a threshold for the response filter to determine that the response strength is invalid if the swipe time becomes an abnormal response time. This allows for the removal of responses with reduced reliability from the personality data.

[0058] Here, the abnormal response time is, for example, the time set by the time setting unit 12. The time setting unit 12 sets the standard response time, but it can also set the abnormal response time at the same time. The abnormal response time refers to a time that is clearly too short and a time that is clearly too long. It is preferable to set an abnormal response time that is optimal for each question.

[0059] The reason for classifying obviously short response times as abnormal response times is that such times may indicate that the respondent was answering playfully or without carefully considering the question. On the other hand, the reason for classifying obviously long response times as abnormal response times is that such times may indicate that the respondent forgot to answer or selected an answer based on criteria different from their true feelings. By removing these times as abnormal response times from the personality data, abnormal noise can be eliminated, thereby increasing the reliability of the personality data.

[0060] The filter setting unit 24 can exclude the question and answer content from the personality data by setting a threshold for the answer filter to determine that the answer strength is invalid if the swipe time is an abnormal response time. The filter setting unit 24 can also set an abnormal flag on the data for the question and answer content where the swipe time is an abnormal response time, indicating that it is an abnormal value.

[0061] The filter setting unit 24 is a device or process for registering the respondent's personality data in a database.

[0062] [1-2. Personality Assessment Methods and Personality Assessment Programs] As shown in Figure 3, the personality diagnostic method includes, for example, a first step S100, a second step S110, a third step S120, a fourth step S130, a fifth step S140, a sixth step S150, a seventh step S160, an eighth step S170, a ninth step S180, and a tenth step S190. The steps may also be referred to as "personality diagnostic steps" or simply "diagnostic steps."

[0063] Furthermore, the personality assessment method is not limited to encompassing all steps; it only needs to include at least one step. Also, the order of the steps is not limited to that shown in Figure 3; the order of the steps may be changed as appropriate, as long as it allows for the use of the results from the previous step.

[0064] Furthermore, the personality assessment method can also be referred to as the personality assessment program. In this case, each step S100 to S190 of the personality assessment method corresponds to each step of the personality assessment program.

[0065] In the first step S100, the measuring unit 14 measures the time required for the swipe operation as the swipe time.

[0066] In the first step S100, the measurement unit 16 takes the lead in measuring, for example, the time required for the respondent to swipe on a touch panel screen as the swipe time. Specifically, the respondent answers questions displayed on the screen of the device or apparatus based on swipe movements on the screen, and the time required for these swipe movements is measured. A swipe movement refers to the action of sliding the respondent's finger across the touch panel screen while it is in contact with the screen. Further details are described in the Personality Diagnostic System 10 and will therefore be omitted here.

[0067] In the second step S110, the determination unit 18 determines the response intensity based on the measurement results obtained in the first step S100.

[0068] In the second step S110, the determination unit 18 takes the lead in determining the response intensity, for example, based on the measurement results from the measurement unit 16 in the first step S100. Specifically, the storage unit 14 stores response intensity determination data or a response intensity determination table for determining the response intensity, and the determination unit 18 uses the response intensity determination data or response intensity determination table to determine the response intensity based on the measurement results from the measurement unit 16. Further details are described in the Personality Diagnosis System 10 and are therefore omitted here.

[0069] In the third step S120, the time setting unit 12 sets a standard response time for each question.

[0070] In the third step S120, the time setting unit 12 takes the lead in outputting questions based on question data previously stored in the memory unit 14, and setting a standard response time for the respondent to answer the question. The standard response time set by the time setting unit 12 is stored in the memory unit 14. Further details are described in the Personality Diagnosis System 10 and are therefore omitted here.

[0071] The fourth step, S130, stores the standard response time and the question content in the memory unit 14, linking them together.

[0072] In the fourth step S130, the memory unit 14 takes the lead, and various data and information necessary for personality diagnosis are stored in the memory unit 14. For example, it is possible to store the standard response time set by the time setting unit 12 and the content of the question at that time in association with it. The memory unit 14 also stores data on the respondent's personal information (address, name, age, etc.) and attributes. Further details are described in the Personality Diagnosis System 10 and will be omitted here.

[0073] In the fifth step S140, the discrimination unit 20 determines the direction of the swipe motion as the answer direction.

[0074] In the fifth step, S140, the discrimination unit 20 takes the lead in determining the direction of the respondent's swipe motion as the response direction. The response direction can also be called the swipe direction. The direction of the swipe motion means the swipe direction. Further details are described in the Personality Diagnosis System 10 and will therefore be omitted here.

[0075] In the sixth step S150, the generation unit 22 generates personality data from the response intensity and response direction.

[0076] In the sixth step S150, the generation unit 22 primarily generates personality data for the respondent from the response intensity determined by the judgment unit 18 and the response direction determined by the discrimination unit 20. By constructing the personality data as response intensity × response direction, it is possible to obtain personality data (big data) composed of many patterns for each respondent. Further details are described in the Personality Diagnosis System 10 and will be omitted here.

[0077] The seventh step, S160, involves associating personality data with the respondent and storing it in the memory unit 14. Here, the memory unit 14 takes the lead, storing various data and a lot of information necessary for personality diagnosis.

[0078] The eighth step, S170, determines the response intensity based on the remaining time obtained by subtracting a predetermined time from the swipe time.

[0079] In the eighth step S170, the determination unit 18 takes the lead in reinforcing the second step S110. As shown in Figure 2, it is preferable that the determination unit 18 determines the response strength based on the remaining time obtained by subtracting a predetermined time (playtime) from the swipe time. This makes it possible to exclude the time it takes for the respondent to start reading and understand the question, and by measuring the true time to answer, the response strength can be determined more accurately. Further details are described in the Personality Diagnostic System 10 and are therefore omitted here.

[0080] In the ninth step S180, the time setting unit 12 sets an abnormal response time for each question.

[0081] Step 9, S180, is, for example, the time set by the time setting unit 12. The time setting unit 12 sets the standard response time, but it can also set an abnormal response time at the same time. An abnormal response time means a time that is clearly too short or clearly too long. It is preferable to set an abnormal response time that is optimal for each question. Further details are described in the Personality Diagnostic System 10 and will be omitted here.

[0082] The tenth step, S190, sets a threshold for the response filter to determine that the response strength is invalid if the swipe time becomes an abnormal response time.

[0083] In the tenth step S190, the filter setting unit 24 plays a central role. The filter setting unit 24 sets a threshold for the response filter to determine that the response intensity is invalid if the swipe time is an abnormal response time, thereby excluding the question content and response content from the personality data. The filter setting unit 24 can also set an abnormal flag in the data file of the question content and response content where the swipe time is an abnormal response time, indicating that it is an abnormal value. Further details are described in the Personality Diagnosis System 10 and are therefore omitted here.

[0084] In the tenth step S190, the filter setting unit 24 may have an abnormal response time set in advance, and may remove the question content and answer content that it determines to be an abnormal response time based on the swipe time. The function of the filter setting unit 24 can also be incorporated as an answer filter function.

[0085] A personality diagnostic method and a personality diagnostic program consisting of at least the first step S100 and the second step S110 is called a personality measurement method, and a program for executing it is called a personality measurement program. Furthermore, a fifth step S140 and a sixth step S150 may be added to the personality measurement method and the personality measurement program.

[0086] According to the first embodiment of the present invention, reliable personality data of respondents can be obtained. Therefore, for example, mismatches between individuals and organizations can be resolved in advance.

[0087] [First Embodiment] Next, embodiments of the personality diagnostic system, personality diagnostic method, and personality diagnostic program according to the first embodiment of the present invention will be described with reference to the drawings.

[0088] (Definition of personality data) Personality data refers to user-specific information obtained from application software, such as thinking patterns, interests, preferences, and decision-making speed.

[0089] (Characteristics of personality data) Regarding personality data, this data is not universal and changes over time. The extent of this change can also be interpreted as part of the personality data itself.

[0090] (Personality assessment scheme) As shown in Figure 4, personality test questions are displayed on the screen of a device (also called a terminal) 30 such as a smartphone, and the user (also called the respondent or test-taker) swipes on the screen to select an answer. Data including the direction of the answer (swipe direction) and the intensity of the answer (time taken to swipe) is saved to the database 34 through the answer filter 32.

[0091] The response filter 32 is an example of the filter setting unit 24 of the embodiment. The database 34 is an example of the storage unit 14 of the embodiment.

[0092] A valid response time range is defined for each question, and the response result within that time range, along with the time taken to answer, is recorded as personality data. At this time, the time taken for the user's swipe action (swipe time) is used to determine the personality of the respondent.

[0093] (Personality diagnostic test procedure) As shown in Figure 5, the user begins taking a personality test, for example, on application software installed on the device (S200).

[0094] The user answers the personality test questions by swiping a card containing the questions to either the left or right (S210). The card is displayed on the device screen, for example.

[0095] Here, if the user swipes to the right on the screen, it indicates that they value the right-hand option on the card more than the left-hand option. In this case, the direction the user swipes is defined as the answer direction, and the time taken to swipe is defined as the answer strength. This combination is then used to determine the answer to that question. For each question, an answer filter 32 is applied (S220) which determines that an answer is invalid if the time taken to swipe is unnaturally short or long. The threshold of the answer filter 32 can be changed depending on the difficulty level and length of each question.

[0096] Personality data that has passed the response filter 32 is stored in database 34, linked to the user (test taker) (S230).

[0097] Personality data is collected for a predetermined number of axes and used as individual personality data (S240). This personality data is used, for example, in the personnel matching process of the second embodiment.

[0098] Traditional personality assessment tests acquired users' continuous and fluctuating personality data in a multiple-choice format, which led to data loss during acquisition and reduced the reliability of the user's personality data.

[0099] To address these issues, as shown in Figure 6, by acquiring the time required to make a decision—which is negatively correlated with the strength of the user's will—as raw data without clustering, it is possible to build a data infrastructure that includes information about the user's personality and prevents data loss.

[0100] As shown in Figure 7, in order to understand the user's circumstances when they respond in detail, information about the surrounding environment of the device (terminal) 30, such as time information and acceleration information, may be acquired and used in addition to swipe status such as swipe speed.

[0101] As shown in Figure 8, the coordinate movement on the device 30 resulting from the swipe may be used as the response direction, and the total time from the display of the swipe target to the completion of the swipe may be obtained as the response intensity, and these may be combined into a single object and passed to the response filter 32.

[0102] As shown in Figures 9 and 10, the logic of the answer filter 32 may have question-specific recognition time and answer difficulty as its principal variables.

[0103] In the response filter 32, in addition to the question-specific information mentioned above, the average response time required for each user may also be used as a secondary variable.

[0104] As shown in Figure 11, the average response time required for each user is not a simple average, but rather a base response time that can vary depending on the surrounding information and time of day during the test. This base response time may be achieved by incorporating an autonomous learning function into the logic.

[0105] As shown in Figure 12, it is possible to apply the personality axis included in each question item, but excessive averaging carries the risk of homogenizing the personality data. Therefore, it is preferable to use it within the range of the response filter 32.

[0106] As shown in Figure 13, data that did not pass the response filter 32 may be retained as archived data without being physically deleted, in order to be used in the learning process of the response filter 32. For this reason, in addition to the personality data DT1, the database 34 may also store archived response data DT2.

[0107] As shown in Figure 14, if there are answer results from other tests with the same personality data structure to be acquired, these may be used as initial values ​​in the parameter tuning of the answer filter 32.

[0108] [Second Embodiment] Next, a second embodiment of the present invention is a personnel matching system, personnel matching method, and personnel matching program that use personality data obtained from the personality measurement system, personality diagnosis system, personality measurement method, personality diagnosis method, and personality diagnosis program according to the first embodiment, and will be described with reference to the drawings.

[0109] [Background leading to the second embodiment] First, we will explain the background leading to the talent matching system, talent matching method, and talent matching program utilizing personality data in the second embodiment. In the so-called seller's market for recruiting new graduates, the percentage of companies whose job offers are declined by new graduates is on the rise. Even when companies do hire new graduates, the number of new graduates who leave their jobs in their first year is increasing year by year. In this situation, the cost of acquiring the talent that companies need in the new graduate market is increasing year by year, and the number of new graduates who leave their jobs soon after being hired is also increasing, making it extremely difficult for companies to recruit students, and new graduate recruitment has become a major management challenge for companies. The main reason students leave their jobs early is a mismatch in interpersonal relationships within the company. More specifically, it stems from the inability to accurately understand individual personalities. Therefore, in corporate recruitment activities for students, avoiding interpersonal mismatches in advance is highly anticipated because it can lead to continued satisfaction for both the company and the students during and after the recruitment process, reduce early turnover among students, and facilitate smooth corporate operations. In this embodiment, we have realized that this problem can be broadly applied not only to the relationship between "students" and "companies," but also to the relationship building between "individuals" and "organizations," "individuals" and "communities," "individuals" and "individuals," and "organizations" and "organizations," and have arrived at the present invention. These corresponding relationships can be directly applied to the "one side" and "the other side" of the present invention.

[0110] The second embodiment of the present invention, which utilizes personality data for personnel matching, personnel matching method, and personnel matching program, utilizes the personality data obtained in the first embodiment and first example. The second embodiment achieves optimal matching between the personality of one side and the personality of the other side. For the sake of explanation, for example, an example in which one side is referred to as the "individual side" and the other side as the "organization side" will be given. However, it is not limited to optimal matching between "individual personality" and "organization personality," but can also be similarly applied to optimal matching between "organization personality" and "organization personality," in addition to optimal matching between "individual personality" and "individual personality."

[0111] A second embodiment of the present invention, comprising a personnel matching system, a personnel matching method, and a personnel matching program utilizing personality data, will be described with reference to the drawings. This personnel matching system may be installed or downloaded onto an existing casing (e.g., hardware such as a mobile phone, smartphone, or personal computer terminal or device), or it may be implemented using the hardware itself (e.g., a personal computer or server).

[0112] More specifically, the personnel matching system, personnel matching method, and personnel matching program according to the second embodiment of the present invention are apparatus, processes, or programs for proposing the optimal match between an individual and an organization by utilizing personality data obtained when a respondent performs a swipe motion (also simply referred to as "swiping") on the screen of a terminal or device such as a smartphone.

[0113] [2-1. Talent Matching System] As shown in Figure 15, the personnel matching system 100 includes, for example, a system body 102 having a storage unit 104 and a matching generation unit 106. The system body 102 is communicated to a personal device 110 on which personal application software 108 (referred to as "personal application 108" as appropriate) is installed, and to an organization device 114 on which organization application software 112 (referred to as "organization application 112" as appropriate) is installed.

[0114] The memory unit 104 corresponds to the memory unit 14 of the first embodiment, and may be a single memory unit that combines both functions. The matching generation unit 106 corresponds to the generation unit 22 of the first embodiment, and may be a single generation unit that combines both functions.

[0115] Furthermore, the personnel matching system 100 or the system body 102 may have the same configuration as the personality diagnosis system 10, or it may be a single system that combines the functions of both.

[0116] In Figure 15, for the sake of explanation, an example is shown in which each part of the personnel matching system 100 is located on a single device or apparatus, but the system is not limited to this configuration. For example, a configuration in which each part is distributed across multiple different devices or apparatuses may be adopted. For example, a single device or apparatus may only house the matching generation unit 106, with the others located on a server or cloud outside the device or apparatus. Alternatively, a single device or apparatus may only house the storage unit 104, with the other parts located on a server or cloud outside the device or apparatus. Furthermore, some of the parts of the personnel matching system 100 may be composed of components such as a CPU, while other parts may be ensured by performing calculation processing using software programs or the like.

[0117] Here, the memory unit 104 may consist of RAM (Random Access Memory), ROM (Read Only Memory), storage, etc.

[0118] The matching generation unit 106 may be composed of, for example, a central processing unit (CPU) or a control unit (controller, not shown). However, it is not limited to these configurations.

[0119] In the first embodiment, the memory unit 104 stores personality data of individuals or organizations obtained by responding to questions with a swipe gesture. Furthermore, the memory unit 104 stores individual-side personality data, where the population is individuals, and organizational-side personality data, where the population is organizations.

[0120] In detail, personal personality data obtained by an individual performing a swipe action from a personal application 108 installed on the personal device 110 is input. Personal personality data consists of data comprising the swipe direction and swipe duration of the individual's swipe action. Similarly, organizational personality data obtained by an organization (such as individuals constituting the organization, or the organization's representative or person in charge) performing a swipe action from an organizational application 112 installed on the organizational device 114 is input. Organizational personality data consists of data comprising the swipe direction and swipe duration of the organization's swipe action. The personal personality data and organizational personality data obtained as described above are stored in the storage unit 104.

[0121] Furthermore, the personality data collected from the organization is not necessarily limited to data obtained through swipe gestures. For example, it may consist of data collected from surveys conducted by the organization, such as attributes, policies, mission, objectives, philosophy, and identification of desired personnel.

[0122] The matching generation unit 106 matches individual personality data with organizational personality data. In other words, the matching generation unit 106 may provide an optimal match between individual personality data and organizational personality data.

[0123] Specifically, the matching generation unit 106 receives personal personality data input from the personal device 110 and organizational personality data input from the organizational device 114, and generates matching data at a predetermined timing. This matching data is output to the personal device 110 via the personal application 108 and to the organizational device 114 via the organizational application 112.

[0124] Here, the matching generation unit 106 may use individual personality data and organization personality data as input data, and use multiple conceivable matching cases as training data, for example, a machine learning-based predictive model, to output multiple matching models as recommended personality data. Specifically, the recommended personality data may be output to the individual application 108 and / or the organization application 112, and information may be acquired via the individual device 110 or the organization device 114.

[0125] Furthermore, the matching generation unit 106 may search for and output content related to talent matching based on the recommended personality data.

[0126] [2-2. Talent Matching Methods and Talent Matching Programs] As shown in Figure 16, the personnel matching method includes, for example, a first step S300, a second step S310, a third step S320, a fourth step S330, a fifth step S340, a sixth step S350, and a seventh step S360. The steps may also be referred to as "personnel matching steps" or simply "matching steps".

[0127] Furthermore, the personnel matching method is not limited to a configuration that covers all steps; it only needs to have at least one step. Also, it is not limited to the order of steps shown in Figure 16, and the order of steps may be changed as appropriate, as long as it allows for the use of the results of the previous step.

[0128] Furthermore, the talent matching method can also be referred to as a talent matching program. In this case, each step S300 to S360 of the talent matching method becomes each step of the talent matching program.

[0129] The first step S300 involves storing in the storage unit 104 the individual-side personality data, which is the population of individuals, and the organizational-side personality data, which is the population of organizations.

[0130] The second step, S310, matches the individual personality data with the organization personality data. The matching of the individual personality data and the organization personality data may be performed by a person in charge, or the matching generation unit 106 may have a matching function.

[0131] In the third step S320, for example, personal personality data input from personal application 108 on personal device 110 and organizational personality data input from organizational application 112 on organizational device 114 may be stored in the storage unit 104.

[0132] In the fourth step S330, the matching generation unit 106 receives the input of individual personality data and organizational personality data and generates matching data.

[0133] The fifth step, S340, outputs the matching data to the individual application 108 and the organization application 112. This allows the matching data to be acquired via the individual device 110 and the organization device 114. Alternatively, the matching data may be output to only one of the individual application 108 or the organization application 112.

[0134] In the sixth step S350, the matching generation unit 106 uses individual personality data and organizational personality data as input data, and uses multiple conceivable matching cases as training data, for example, a machine learning-based predictive model, to generate and output multiple matching models as recommended personality data. This allows matching data to be acquired via individual devices and organizational devices. Alternatively, the recommended personality data may be output to only one of the individual application 108 or the organizational application 112.

[0135] Here, the recommended personality data may be output together with the matching data, or in place of the matching data.

[0136] In the seventh step, S360, the matching generation unit 106 searches for and outputs content related to personnel matching based on the recommended personality data.

[0137] According to a second embodiment of the present invention, an optimal matching model between individuals and organizations can be provided by utilizing the respondent's reliable personality data. Simultaneously, because the matching generation unit 106 has its own AI function, it can receive input of individual-side personality data and organization-side personality data, and use multiple conceivable matching cases as training data, for example, using a machine learning-based predictive model, to generate and output recommended personality data. As a result, mismatches between individuals and organizations can be reliably resolved. Furthermore, since the matching generation unit 106 searches for and outputs content related to talent matching based on the recommended personality data, individuals or organizations can refer to and share the most suitable content.

[0138] [First Embodiment] Next, embodiments of a second embodiment of the present invention relating to a personnel matching system, a personnel matching method, and a personnel matching program will be described with reference to the drawings.

[0139] (Overview of talent matching) In the field of human resources, this approach aims to improve user engagement and well-being (both individual and organizational) by providing content matching to users based on either individual or organizational personality, or both, using, for example, machine learning.

[0140] (Procedure for talent matching) (1) Collect user personality data through personal applications (reference numeral 116 in Figure 17) or SaaS for organizations (reference numeral 122 in Figure 17). (2) Personality data of individuals or organizations (indicated by symbols 118 and 124 in Figure 17) is stored in a database (indicated by symbol 120 in Figure 17). (3) Personality data (118, 124 in Figure 17) stored in the database (120 in Figure 17) is input to the matching generator (126 in Figure 17), and the matching generator 126 outputs matching content related to career and personnel (128 in Figure 17). (4) Deliver the matched content to the user using a personal application (reference numeral 116 in Figure 17) or a SaaS for organizations (reference numeral 122 in Figure 17).

[0141] (Talent matching scheme) The logic of this embodiment incorporates artificial intelligence, either partially or entirely. It takes individual and organizational personality data as input and outputs the personalities to be matched. It then performs content matching based on the recommended personality data.

[0142] As shown in Figure 17, for example, personal user information 118 is stored in the database 120 from personal software 116. Similarly, for example, organizational personality information 124 is stored in the database 120 from organizational software 122. In the matching generator 126, content matching 128 is performed based on the personal user personality information 118 and the organizational personality information 124. The content matching results are output to personal software 116 and organizational software 122.

[0143] Note that "Personal Software 116" in Figure 17 corresponds to "Personal Application 108" shown in Figure 15. "Organizational Software 122" in Figure 17 corresponds to "Organizational Application 112" shown in Figure 15. "Matching Generator 126" in Figure 17 corresponds to "Matching Generation Unit 106" shown in Figure 15.

[0144] (Procedure for talent matching) As shown in Figure 18, artificial intelligence is trained. Personality data of individuals or organizations is prepared as input, and assumed matching examples are prepared as training data, and the model is trained based on several common learning methods (S400). Here, the input training data changes depending on the use case of the matching generator 126. The matching generator 126 may prepare multiple models and perform different training depending on the use case. The matching generator 126 takes data that is formally the same as the data used for training in S400 as input, and outputs the matching target in the form of personality data (S410). The matching generator 126 searches for content based on the personality data and outputs it (S420). Based on user feedback (FB), data to be used for training in S400 is extracted, and by repeatedly performing steps S400 to S420, the accuracy of the model can be improved. While supervised learning is given here as an example, other machine learning logics such as unsupervised learning or deep learning may also be applied. Furthermore, in each embodiment, various other models such as k-means algorithms or factor analysis may be used in addition to machine learning.

[0145] Figure 19 is a diagram illustrating the machine learning scheme that utilizes recommended personalities. As shown in Figure 19, the personality information of individual users 118 and the personality information of organizations 124 are input to determine the recommended personality 130. Based on the recommended personality, the matching generator 126 performs content matching 128. Subsequently, feedback (FB) 132 from the user is obtained, and various personality information 118 and 124 are further updated.

[0146] Figure 20 is a diagram illustrating the machine learning scheme that utilizes evaluation scores. As a new output pattern, instead of the similarity search for recommended personalities 130 shown in Figure 19, a talent matching model that takes both sets of personality information 118 and 124 as input and uses a matching target variable (evaluation score) 134 for matching as output is also necessary for lightweight use cases.

[0147] (Definition of optimal match) Next, we will explain the logic for defining the optimal match. This involves considering the various uses and contexts of matching an organization with individuals and sub-organizations within it. The main factors that determine whether a particular match is a "good match" are primarily as follows: (1) Training data indicating that users reacted with "Good match" (2) Typical organizational characteristics include, for example, sales organizations or development organizations, and are linked to the purpose and role of the organization. (3) Organization-specific characteristics include, for example, the organization's unique corporate culture, which is linked to the organization's current state (see Figure 21).

[0148] A model that combines both general organizational characteristics and organization-specific characteristics serves as the basis for a model "under a specific purpose for a particular organization." Machine learning can then be introduced into this base based on user reactions to fine-tune the details.

[0149] As shown in Figure 22, when the weight function applied to each of the n axes is analyzed at the point when it becomes possible to determine a "good match" after the adjustments, the weight function parameters for those n items represent the "individuality" of that organization within the overall organization.

[0150] (A logic for matching individuals (1-on-1 personality matching)) Under specific conditions, this involves matching a candidate's personality with the personality data of other individuals, or selecting the best match. One example of this is assigning an appropriate mentor or supervisor to an individual.

[0151] For two personalities, if training data is insufficient, set an axis that emphasizes convergence or variance, which seems appropriate for several contexts, for the n-axis correlation. For example, assuming that it is desirable for extroversion in a sales organization to converge towards extroversion, a match in this context would be strongly rated as a good match if the extroversion is towards extroversion.

[0152] (Matching algorithm between individuals and organizations (1-n matching algorithm)) As shown in Figure 23, this includes cases where an individual is matched with a subsidiary organization within an organization, as well as cases where an individual is matched with an organization outside of an organization. For example, this applies to the placement of new employees.

[0153] (1) Determine "what constitutes a good match" according to the intended use (see definition of optimal match). Determine the base model to be used. (2) Based on the base model determined above, network data is constructed for the collection of personality data of the organization. The network model constructed will vary depending on the base model chosen. (3) When personal data is added as a new node to the constructed network structure, the system scans for the individual to be added or the organization to which it will be added, with the aim of ensuring a better match. (4) The target that is evaluated as the best match under the objective may be defined as the match result. If objective answer data such as on-site evaluations is available, machine learning may be used in the logic. (5) Personality data is time-series data, and therefore changes over time at the individual or organizational level.

[0154] Here, some aspects, such as interests, change rapidly, while others, such as innate traits, change more slowly. When an individual experiences a paradigm shift in their interests due to external factors, it is possible to predict favorable timing for organizational change through regression analysis with time series data. It is also possible to incorporate self-learning capabilities into personality data. Therefore, if sufficient training data is obtained, the system can proactively recommend matches at the appropriate time.

[0155] [Examples of personality profile generation] Next, we will describe examples of personality image generation in personality matching obtained using the personality measurement system, personality diagnosis system, personality measurement method, personality diagnosis method, and personality diagnosis program according to each embodiment of the present invention.

[0156] A. Examples of generation in relationship analysis By analyzing the relationships between each personality trait and specific organizational key performance indicators (KPIs) using analytical methods such as factor analysis, correlation analysis, and network analysis, specific personality traits that influence the KPIs are identified. These personality traits are then combined to generate a personality profile. Examples of KPIs are as follows: (a) Evaluation (i) Grades (c) Performance (e) Engagement (O) stress (c) Other survey results

[0157] B. Examples of generation based on trend analysis Personality tendencies are analyzed for a specific group of individuals, identifying personality traits that are extremely strong or extremely weak. These personality traits are then combined to generate a personality profile. Examples of groups are as follows: (a) By position, such as executive officers, managers, and management staff (i) By KPI, such as top performers and high performers (c) By region, area, etc., such as head office and branch offices (e) Departments such as Sales Department 1, Management Department, etc. (e) By job type, such as new business development, accounting, and customer support. (c) By entry date and length of service for new employees, senior employees, etc. (k) By employment type, such as full-time employee, contract employee, part-time employee, and temporary worker.

[0158] C. Examples of generation based on individual analysis Personality is analyzed for a specific individual to identify personality traits that are extremely strong or extremely weak. These personality traits are then combined to generate a personality profile. Alternatively, all personality traits can be utilized. Examples of individuals who are included in this group are as follows: (a) Management (a) Corporate representative (b) Directors (c) Officer (d) Personnel (e) Organizational representative (f) Branch office representative (g) Branch Representative (i) By KPI (a) Excellent grades (b) High performers (c) By role (a) Leader (b) New employees (c) Mentor (d) Mentee

[0159] [Examples of how personality matching can be used] Next, we will describe examples of how personality matching can be utilized using the personality measurement system, personality diagnosis system, personality measurement method, personality diagnosis method, and personality diagnosis program according to each embodiment of the present invention.

[0160] A. Recruitment matching (a) Method We select and match candidates from a pool who have a high degree of compatibility with the organization's ideal personality profile. Alternatively, we provide recruitment, staffing, and placement services. (i) Effects This prevents mismatches in recruitment, leading to improved employee retention and a reduction in early employee turnover.

[0161] B. Placement Matching (a) Method We select employees from other organizations who have a high degree of compatibility with the organization's ideal personality profile and then match them with the organization. (i) Effects Optimizing overall personnel allocation leads to increased productivity.

[0162] C. Personal Matching (a) Method Individuals with similar personalities are selected and matched. (i) Effects By utilizing mentor-mentee selection and counseling, mismatches can be prevented.

[0163] D. Content Matching (a) Method This system compares a specific individual's personality to the organization's ideal personality and extracts discrepancies. It then matches the individual with content designed to influence those specific personality traits, enabling the provision of personalized training methods and content. (i) Effects By reducing the discrepancy between current employees and their ideal personalities and bringing them closer to those ideals, we aim to improve the retention rate and productivity of existing employees.

[0164] [Utilization of analysis results and personnel selection by area managers] Next, we will explain how area managers can utilize the analysis results and personnel selection processes using the personality measurement system, personality diagnosis system, personality measurement method, personality diagnosis method, and personality diagnosis program according to each embodiment of the present invention.

[0165] (Current challenges) Although we select area managers based on their experience and intuition as store managers, the required temperament and skills for the area manager position are completely different, and as a result, they are not performing as expected.

[0166] (Purpose of the initiative) We clarified the relationship between "personality" and "evaluation" and explored whether this could be used to select suitable personnel for the role of area manager.

[0167] (Acquired data) (1) Personality survey This diagnostic tool was actually developed independently by blankpad Co., Ltd., the applicant of this application, under the supervision of a professor of personality psychology at Waseda University, and aims for a multi-layered and multifaceted understanding that applies not only to the individual level but also to the organizational level. (2) Evaluation A 5-point rating system (C, B-, B, B+, A) was used. (3) Stress survey This questionnaire is based on a simplified occupational stress questionnaire developed in accordance with recommendations from the Ministry of Health, Labour and Welfare, and aims to assess stress levels. It is a multi-axis questionnaire that simultaneously measures not only stress responses but also work-related stressors and modifying factors. Furthermore, in the psychological stress response section, it can measure not only negative responses but also positive responses that contribute to performance improvement.

[0168] (Analysis method) (1) Trend Analysis We analyzed the characteristic traits common to all area managers, focusing on their personality and stress levels. (2) Correlation analysis We analyzed the strength of the relationships between personality and evaluation, personality and stress, and evaluation and stress.

[0169] (Analysis results - Personality tendencies) The current trend among area managers is to exhibit a practical and highly adaptable leadership style, excelling in daily work management and building interpersonal relationships. (Personality item) - (Score) - (Interpretation) (Strong sense of efficiency) - (3.86 / 5) - (Prioritizes time efficiency and practicality over aesthetic sensibility) (Strength of expression of respect) - (3.82 / 5) - (Having an attitude of showing respect and consideration towards others) (Strength of intuition) - (3.59 / 5) - (Emphasis on feelings and practical experience, and makes judgments based on concrete facts) (Level of curiosity) - (3.55 / 5) - (Curious about new ideas and experiences, and open to change) (comment) The score, ranging from 0 to 5, indicates how strong the player is. A score of 3.5 or higher indicates a very strong tendency and is therefore selected.

[0170] (Analysis results - Correlation between personality and evaluation) Three qualities—a broad perspective, a strong sense of deadlines, and a strong sense of responsibility—are strongly correlated with high evaluations of area managers. (Personality item) - (Correlation coefficient with evaluation) - (Interpretation) (Broad perspective) - (0.62) - (Emphasis is placed on viewing things from a broader viewpoint rather than a narrow one) (Strong sense of deadlines) - (0.62) - (Prioritizes meeting deadlines and achieving goals, and places importance on time management) (Strong sense of responsibility) - (0.59) - (Has a strong sense of responsibility and fulfills their role well) (comment) Based on the total number of test takers, a correlation coefficient of 0.5 or higher is used as the baseline, and items that exceed this threshold in both evaluation and stress levels are selected as important personality items.

[0171] (Analysis results - Current status of correlated personality items) When looking at the evaluations for each personality item, area managers with high evaluations (B+ or A) have higher scores than area managers with low evaluations (C or B-). (Personality item) - (Average score by evaluation) - (Difference (A) - (B)) (Strength of responsibility) - (4.20 for a B+ or A rating, 1.73 for a C or B- rating) - (+2.47 for area managers with high ratings) (Strength of deadline awareness) - (4.20 for a B+ or A rating, 2.27 for a C or B- rating) - (+1.93 for area managers with high ratings) (Breadth of Perspective) - (3.60 for a B+ or A rating, 2.77 for a C or B- rating) - (+0.83 for area managers with high ratings)

[0172] Next, we will explain the expected future applications of the personality measurement system, personality diagnosis system, personality measurement method, personality diagnosis method, and personality diagnosis program according to each embodiment of the present invention.

[0173] [Future Use Prospects] By incorporating a selection process that checks whether the candidate matches the personality traits necessary for area managers to receive high evaluations (i.e., good performance), it becomes possible to select the right personnel. As for the specific application flow, when using it to select area managers, the following flow is envisioned, similar to that of a regular aptitude test. Diagnostic Examination: Candidates for area supervisor positions will take a personality assessment similar to the one being conducted. Check the degree of match in the report: Based on the selected axes, check the degree of match and the points that match and do not match in the report. Verification during the selection process: This information will be used when actually selecting candidates for area supervisors. The selection criteria are as follows: Numbers 4-5 were added because they showed a correlation with evaluation. Numbers 1-3 were given a stronger weight because they showed a correlation with both evaluation and stress. No.1 One personality trait is a strong sense of deadlines. In other words, it emphasizes deadlines and goal achievement, and places great importance on time management. No.2 One of the personality traits is a strong sense of responsibility. One interpretation is that they have a strong sense of responsibility and fulfill their roles well. No.3 One of the personality traits is broad perspective. One interpretation is that it places emphasis on activities abroad and cross-cultural exchange. No. 4 One of the personality traits is a high level of logical thinking ability. One interpretation is that they are interested in natural science and technology, and value logic and experimentation. No. 5 One of the personality traits is high creativity. In interpretation, this means emphasizing creative ideas and expression, and valuing new concepts.

[0174] (Analysis results - Correlation between stress and evaluation) Three traits that have a strong correlation with performance evaluation—"a strong sense of responsibility," "a strong sense of deadlines," and "a broad perspective"—also have a high correlation with low stress levels. (Personality item) - (Correlation coefficient with stress) - (Interpretation) (Strong sense of responsibility) - (0.63) - (Has a strong sense of responsibility and fulfills their role well) (Strong sense of deadlines) - (0.62) - (Prioritizes meeting deadlines and achieving goals, and places importance on time management) (Broad perspective) - (0.51) - (Emphasis is placed on viewing things from a broader viewpoint rather than a narrow one) (comment) Based on the total number of test takers, a correlation coefficient of 0.5 or higher is used as the benchmark, and items that exceed this benchmark in both evaluation and stress levels are selected as important personality items.

[0175] (Analysis results - Correlation between personality and evaluation) In addition, "high level of logical thinking ability" and "high level of creativity" are highly correlated with high evaluations of area managers. (Personality item) - (Correlation coefficient with evaluation) - (Interpretation) (Level of logical thinking ability) - (0.53) - (Interested in natural science and technology, and values ​​logic and experimentation) (High level of creativity) - (0.52) - (Values ​​creative ideas and expression, and cherishes new concepts) (comment) Based on the total number of test-takers, items with a correlation coefficient exceeding 0.5 are judged to have a strong correlation and are selected as personality items.

[0176] (Analysis results - Stress tendencies) The company possesses strengths such as positive interpersonal relationships, a strong support system, and high job satisfaction. These factors are believed to support satisfaction and productivity even under high-stress conditions. On the other hand, excessive workload and the resulting stress responses are major challenges. (List of items with good performance) - (compared to the national average) (Support from superiors) - (30% increase) (Stress due to work environment) - (26% increase) (Job satisfaction) - (21% increase) (Support from colleagues) - (20% increase)

[0177] (List of items that need improvement) - (compared to the national average) (Perceived physical burden) - (32% ↓) (Psychological workload (quality)) - (26% ↓) (Psychological workload) - (24% decrease) (comment) We extracted all aspects that were 20% above the national average and categorized them into positive items and items that need improvement.

[0178] (Analysis results - Relationship between stress and evaluation) Overall, there is a strong tendency for people with lower stress levels to give higher ratings. In particular, those who are able to maintain a better mental and physical state (in terms of the mental and physical reactions caused by stress) tend to receive higher ratings. (List of items) - (Correlation coefficient) (Total stress score) - (0.62) (Physical and mental reactions caused by stress) - (0.60) (Factors considered to be causes of stress) - (0.54) (Other factors influencing stress) - (0.42) (comment) Based on the total number of test-takers, a correlation coefficient of 0.5 or higher is used as the benchmark, and traits that exceed this benchmark in both evaluation and stress levels are selected as important temperaments.

[0179] [List of stressors] (Physical and mental reactions caused by stress) • Irritation • Fatigue • Anxiety • Depression • Physical complaints (Factors that are thought to cause stress) • Psychological workload (amount) • Psychological workload (quality) • Perceived physical burden • Stress from interpersonal relationships at work • Stress caused by the work environment • Degree of control over work • Sense of skill utilization • Suitability for the job Job satisfaction (Other factors that influence stress) • Support from my supervisor • Support from colleagues Support from family and friends • Satisfaction

[0180] The above embodiments are merely examples of the present invention, and the scope of design modifications that those skilled in the art can make as appropriate is included within the scope of the present invention. [Explanation of symbols]

[0181] 10 Personality Diagnostic System 11 Personality Measurement System 12-hour setting section 14 Storage section 16 Measurement Unit 18 Judgment section 20 Discrimination part 22 Generation part 24 Filter setting section 30 devices 32. Answer Filter (Filter Settings Section) 34. Database (storage unit) 100 Talent Matching Systems 102 System Unit 104 Storage section 106 Matching Generation Unit 108 Personal Application Software 110 Personal devices 112 Organization-side application software 114 Organization-side devices 116 Personal Software 118 Personal user personality information 120 Database (storage unit) 122 Software for Organizations 124 Organizational Personality Information 126 Matching Generator 128 Content Matching 130 Recommended Personalities 132 user feedback (FB) 134 Matching objective variable (evaluation score)

Claims

1. A talent matching system that utilizes personality data obtained by answering questions, A storage unit that stores the personality data of one side, which is the population of one side, and the personality data of the other side, which is the population of the other side. A matching generation unit that matches the personality data of one side with the personality data of the other side, A talent matching system that utilizes personality data.

2. A one-sided application that inputs the aforementioned one-sided personality data, The other-side application that inputs the aforementioned other-side personality data, It has, A personality data-utilizing personnel matching system according to claim 1, wherein the personality data of one side input from the one-side application and the personality data of the other side input from the other-side application are stored in a storage unit.

3. The matching generation unit receives the input of the one-side personality data and the other-side personality data, generates matching data, and outputs it to the one-side application and the other-side application, respectively, in the personality data utilization personnel matching system according to claim 2.

4. A method of talent matching that utilizes personality data obtained by answering questions using a talent matching system, A first step of storing the personality data, which is the population of one side of the personality data, and the personality data of the other side, which is the population of the local side of the personality data. A second step of matching the personality data of one side with the personality data of the other side, A talent matching method that utilizes personality data.

5. A one-sided application that inputs the aforementioned one-sided personality data, The other-side application that inputs the aforementioned other-side personality data, Using A method for matching personnel using personality data according to claim 4, further comprising a third step of storing the one-side personality data input from the one-side application and the other-side personality data input from the other-side application.

6. A fourth step involves receiving the aforementioned one-sided personality data and the aforementioned other-sided personality data to generate matching data, A fifth step is to output the matching data to the one-side application and the other-side application, respectively. A method for matching personnel using personality data as described in claim 5, comprising:

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