Organizational exodus prediction support program, trained model creation program, organizational exodus prediction support device, organizational exodus prediction support method, and recording medium

Abstract

We provide a program that uses biometric authentication technology to help predict the likelihood of an individual leaving a particular organization. [Solution] The organizational departure prediction support program of this disclosure includes a data acquisition procedure and a prediction procedure, wherein the data acquisition procedure acquires data including identification data of a person and personal data of the person, the identification data includes data acquired by an imaging means, and the prediction procedure predicts the likelihood that the person will leave a particular organization based on the acquired data, and is a program that causes a computer to execute each of the above procedures.

Description

Technical Field

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[0001] The present disclosure relates to an organization withdrawal prediction support program, a learned model creation program, an organization withdrawal prediction support device, an organization withdrawal prediction support method, and a recording medium.

Background Art

[0002] Patent Document 1 discloses an information processing apparatus including an image acquisition unit that acquires a captured image of a user, a registered user information storage unit that stores face identification data of a registered user, a face recognition unit that uses the face identification data stored in the registered user information storage unit to detect a face image of the registered user present in the captured image, and an information processing unit that performs information processing based on the detection result by the face recognition unit, wherein the face identification data includes a face image of the user captured in advance and information on a processed image obtained by subjecting the face image to a predetermined process.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Biometric authentication technologies such as face authentication disclosed in Patent Document 1 are widely used in fields such as work attendance management and leaving management, but there is no case where they are used as means for predicting the possibility of withdrawal from a certain organization such as resignation or dropping out of school.

[0005] Therefore, an object of the present disclosure is to provide an organization withdrawal prediction support program, a learned model creation program, an organization withdrawal prediction support device, an organization withdrawal prediction support method, and a recording medium for supporting the prediction of the possibility of withdrawal from a certain organization using biometric authentication technology.

Means for Solving the Problems

[0006] To achieve the aforementioned objectives, the organizational exodus prediction support program of this disclosure is: Including data acquisition procedures and prediction procedures, The aforementioned data acquisition procedure acquires data including identification data of a person and personal data of the person, The aforementioned identification data includes data acquired by the imaging means, The aforementioned prediction procedure predicts the likelihood that the person will leave a particular organization based on the acquired data. This is a program that causes a computer to execute each of the aforementioned steps.

[0007] The trained model creation program described herein is Includes the procedure for creating a pre-trained model, The aforementioned pre-trained model creation procedure creates a pre-trained model that learns based on training data including person identification data and the person's personal data, and when the data including the identification data and the personal data is input, it outputs a prediction result of the likelihood that the person will leave a particular organization. The aforementioned identification data includes data acquired by the imaging means, This is a program that causes a computer to execute each of the aforementioned steps.

[0008] The organizational departure prediction support device disclosed herein, Including a data acquisition unit and a prediction unit, The data acquisition unit acquires data including identification data of a person and personal data of the person. The aforementioned identification data includes data acquired by the imaging means, The prediction unit is a device that predicts the likelihood of a person leaving a particular organization based on the acquired data.

[0009] The organizational departure prediction support method described in this disclosure is: Including a data acquisition process and a prediction process, The data acquisition step acquires data including identification data of a person and personal data of the person. The aforementioned identification data includes data acquired by the imaging means, The prediction step predicts the likelihood that the person will leave a particular organization based on the acquired data. This method involves each of the aforementioned steps being performed by a computer.

[0010] The recording medium of this disclosure is a computer-readable recording medium on which the program of this disclosure is recorded. [Effects of the Invention]

[0011] According to this disclosure, it is possible to provide an organization departure prediction support program, a trained model creation program, an organization departure prediction support device, an organization departure prediction support method, and a recording medium for supporting the prediction of the likelihood of leaving a certain organization using biometric authentication technology. [Brief explanation of the drawing]

[0012] [Figure 1] Figure 1 is a block diagram showing the configuration of an example of the organizational departure prediction support device of this disclosure. [Figure 2] Figure 2 is a block diagram showing an example of the hardware configuration of the organizational departure prediction support device of this disclosure. [Figure 3] Figure 3 is a flowchart illustrating an example of the procedure in the organizational exodus prediction support program described herein. [Figure 4] Figure 4 is a block diagram showing the configuration of another example of the organizational departure prediction support device of this disclosure. [Figure 5] Figure 5 is a flowchart illustrating another example of the procedure in the organizational exodus prediction support program described herein. [Figure 6] Figure 6 is a block diagram showing the configuration of an example of a trained model creation device according to this disclosure. [Figure 7] Figure 7 is a block diagram showing an example of the hardware configuration of the trained model creation device of this disclosure. [Figure 8]FIG. 8 is a flowchart showing an example of a procedure in the trained model creation program of the present disclosure. [Figure 9] FIG. 9 is a schematic diagram showing an example of a method for supporting prediction of the possibility of withdrawal from a certain organization by the organization withdrawal prediction support device of the present disclosure. **Embodiments for Carrying Out the Invention**

[0013] Embodiments of the present disclosure will be described. Note that the present disclosure is not limited to the following embodiments. In the following figures, the same parts are denoted by the same reference numerals. Also, the descriptions of the respective embodiments can be mutually referred to unless otherwise specified. Furthermore, the configurations of the respective embodiments can be combined unless otherwise specified. In addition, each procedure described later in the program of the present disclosure can be read as "processing" instead of "procedure", for example. [[ID=……]]

[0014] [Embodiment 1] The organization withdrawal prediction support program, the organization withdrawal prediction support device, and the organization withdrawal prediction support method of the present disclosure will be described.

[0015] The organization withdrawal prediction support program of the present disclosure is a program for causing a computer to execute a data acquisition procedure and a prediction procedure. The organization withdrawal prediction support program of the present disclosure can also be said to be a program for causing a computer to function as a data acquisition procedure and a prediction procedure. In addition, the organization withdrawal prediction support program of the present disclosure can also be said to be a program for causing a computer to execute each step of the organization withdrawal prediction support method described later, for example.

[0016] Next, an example of the organization withdrawal prediction support device of the present disclosure will be described based on FIGS. 1 and 2.

[0017] FIG. 1 is a block diagram showing an example of the configuration of the organization withdrawal prediction support device 10 (this device 10) of the present disclosure. As shown in FIG. 1, this device 10 includes a data acquisition unit 11 and a prediction unit 12.

[0018] The device 10 may be, for example, a single device including the aforementioned parts, or it may be a device in which the aforementioned parts can be connected via a communication network. Furthermore, the device 10 can be connected to an external device described later via the communication network. The communication network is not particularly limited and can use a known network, for example, it may be wired or wireless. Examples of the communication network include the Internet, WWW (World Wide Web), telephone lines, LAN (Local Area Network), SAN (Storage Area Network), DTN (Delay Tolerant Networking), LPWA (Low Power Wide Area), L5G (Local 5G), etc. Examples of wireless communication include Wi-Fi (registered trademark), Bluetooth (registered trademark), Local 5G, LPWA, etc. The wireless communication may be in the form of direct communication between devices (Ad Hoc communication), infrastructure communication, indirect communication via an access point, etc. The device 10 may be, for example, incorporated into a server as a system. Furthermore, the device 10 may be, for example, a personal computer (PC, e.g., desktop or notebook), smartphone, tablet terminal, digital signage, etc., on which the program disclosed herein is installed. The device 10 may also be in the form of cloud computing or edge computing, for example, in which at least one of the aforementioned parts is on a server and the other parts are on a terminal.

[0019] Figure 2 illustrates a block diagram of the hardware configuration of the device 10. The device 10 includes, for example, a central processing unit (CPU, GPU, etc.) 101, memory 102, bus 103, storage device 104, input device 105, output device 106, communication device 107, etc. Each part of the device 10 is interconnected via the bus 103 through its respective interface (I / F).

[0020] The central processing unit 101 operates in coordination with other components via controllers (system controller, I / O controller, etc.) and is responsible for the overall control of the device 10. In the device 10, the central processing unit 101 executes, for example, the program disclosed herein and other programs, and also reads and writes various types of information. Specifically, for example, the central processing unit 101 functions as a data acquisition unit 11 and a prediction unit 12. The central processing unit 101 may include a CPU, a GPU (Graphics Processing Unit), an APU (Accelerated Processing Unit), or a combination thereof as its computing device.

[0021] Bus 103 can also be connected to external devices, for example. Examples of such external devices include external storage devices such as external databases, printers, external input devices, external display devices, and external imaging devices. The device 10 can be connected to an external network (the aforementioned communication network) by a communication device 107 connected to bus 103, for example, and can also be connected to other devices via the external network.

[0022] Memory 102 may be, for example, main memory. When the central processing unit 101 performs processing, memory 102 reads various operational programs, such as the program of this disclosure, stored in the storage device 104 (described later), and the central processing unit 101 receives data from memory 102 and executes the program. The main memory may be, for example, RAM (random access memory). Alternatively, memory 102 may be, for example, ROM (read-only memory).

[0023] The storage device 104 is also called an auxiliary storage device, for example, in relation to the main memory (primary memory). As described above, the storage device 104 stores an operating program including the program of this disclosure. The storage device 104 may be, for example, a combination of a recording medium and a drive for reading and writing to the recording medium. The recording medium is not particularly limited and may be internal or external, for example, an HD (hard disk), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, memory card, etc. The storage device 104 may be, for example, a hard disk drive (HDD) in which the recording medium and the drive are integrated, or a solid state drive (SSD).

[0024] In this device 10, the memory 102 and storage device 104 can also store various types of information, such as log information, information obtained from an external database (not shown) or external devices, information generated by this device 10, and information used by this device 10 when executing processing. In this case, the memory 102 and storage device 104 may store, for example, identification data, personal data, location information data, etc., as described later. At least some of the information may be stored on an external server other than the memory 102 and storage device 104, or it may be stored in a distributed manner across multiple terminals using blockchain technology or the like.

[0025] The device 10 further includes, for example, an input device 105 and an output device 106. The input device 105 may include, for example, a pointing device such as a touch panel, trackpad, or mouse; a keyboard; imaging means such as a camera or scanner; a card reader such as an IC card reader or magnetic card reader; an audio input means such as a microphone; and so on. The output device 106 may include, for example, a display device such as an LED display or liquid crystal display; an audio output device such as a speaker; a printer; and so on. In this disclosure, the input device 105 and the output device 106 are configured separately, but the input device 105 and the output device 106 may be configured as an integrated unit, such as a touch panel display.

[0026] First, an example of the processing of the organizational exodus prediction support program described in this disclosure will be specifically explained based on Figure 3. Figure 3 is a flowchart showing an example of each step of the organizational exodus prediction support program described in this disclosure.

[0027] The data acquisition unit 11 acquires data including identification data of a person and personal data of the person (S11, data acquisition procedure). Examples of the person include students, employees, staff, members, trainees, club members, union members, organization members, and team members.

[0028] The aforementioned identification data includes data acquired by the imaging means. Examples of the aforementioned identification data include, but are not limited to, name, age, address, telephone number, email address, date of birth, personal identification ID (identification), clothing, hair color, height, and build. The aforementioned identification data may also include, for example, data based on biometric authentication, including facial recognition. The imaging means may be, for example, an imaging means provided by the device 10, or an imaging means other than that of the device 10. The imaging means may be, for example, a camera. The camera may be, for example, a portable camera or a stationary camera. Examples of portable cameras include cameras built into mobile phones, tablets, and personal computers. Examples of stationary cameras include cameras installed in any location such as schools, offices, factories, commercial facilities, and sports facilities.

[0029] The aforementioned personal data includes, for example, information relating to at least one of the following: physical characteristics, activity history, and state. Examples of information relating to the physical characteristics include biometric information such as heart rate, body temperature, respiratory rate, blood pressure, and pulse. Examples of information relating to the activity history include activity history in an organization described later. Examples of information relating to the activity history include information relating to attendance, such as attendance, absence, tardiness, and early departure; information relating to participation status, such as participation or non-participation; information relating to work status, such as absence, attendance, and leave; and information relating to evaluation, such as grades, performance, scores, results, progress, and achievement rates. Examples of information relating to the state include information relating to concentration level, drowsiness, physical or mental fatigue level, stress level, alertness, attention level, relaxation level, tension level, motivation, psychological load, emotions, judgment, interests, and cognitive function. The aforementioned personal data may be, for example, data acquired by the imaging means, or data acquired by other means. Other means mentioned above include, for example, wearable devices, inspection equipment, inspection monitors, measuring instruments, and various trackers.

[0030] The identification data and personal data may be acquired simultaneously or separately. When acquired simultaneously, for example, the identification data and personal data may be acquired simultaneously by the imaging means. When acquired separately, for example, the personal data may be acquired triggered by the acquisition of the identification data. For example, when the identification data is acquired, the other means may acquire the personal data in conjunction with the acquisition of the identification data. The personal data linked to the identification data may be acquired from a database. The database may be provided by the device 10 or by another device.

[0031] The prediction unit 12 predicts the likelihood that the person will leave a particular organization based on the acquired data (S12, prediction procedure). The particular organization may be, for example, a school, company, club, association, union, project, or team. The departure may be, for example, dropping out of school, taking a leave of absence, resigning, taking a leave of absence from work, withdrawing from a club, or leaving the organization.

[0032] The prediction unit 12 performs predictions using, for example, a pre-trained model. The pre-trained model is, for example, trained on training data including the identification data and the personal data, and when data including the identification data and the personal data is input, it outputs a prediction result of the likelihood that the person will leave a particular organization.

[0033] According to the organizational departure prediction support program of this disclosure, the data acquisition procedure acquires data including identification data and personal data of a person, the identification data including data acquired by imaging means, and the prediction procedure predicts the likelihood that the person will leave a particular organization based on the acquired data. This makes it possible to support the prediction of the likelihood of leaving a certain organization using biometric authentication technology. Based on the prediction results, for example, mental counseling or matching between people can be supported.

[0034] Next, the organizational exodus prediction support method described in this disclosure will be explained. The organizational exodus prediction support method described in this disclosure can be implemented by referring to the descriptions in the organizational exodus prediction support program and organizational exodus prediction support device described in this disclosure. The organizational exodus prediction support method described in this disclosure is a method implemented by, for example, replacing each "procedure" in the program described in this disclosure with a "process". Specifically, the organizational exodus prediction support method described in this disclosure includes a data acquisition process and a prediction process. The organizational exodus prediction support method described in this disclosure can be implemented, for example, using the device 10 in Figure 1. However, the organizational exodus prediction support method described in this disclosure is not limited to the use of the device 10 in Figure 1.

[0035] [Embodiment 2] Further explanation is provided regarding the organizational exodus prediction support program, organizational exodus prediction support device, and organizational exodus prediction support method described herein.

[0036] The organization exodus prediction support program of this disclosure is a program that causes a computer to perform at least one of a detection procedure and an output procedure. The organization exodus prediction support program of this disclosure can also be described as a program that causes a computer to function as at least one of a detection procedure and an output procedure. Furthermore, the organization exodus prediction support program of this disclosure can also be described as a program that causes a computer to perform each step of the organization exodus prediction support method described later.

[0037] Next, another example of the organizational departure prediction support device described herein will be explained with reference to Figure 4.

[0038] Figures 4(A) and 4(B) are block diagrams showing the configuration of an example of the organizational departure prediction support device 10A and 10B (devices 10A and 10B) of the present disclosure. As shown in Figure 4(A), device 10A includes a detection unit 13 in addition to the configuration of device 10. As shown in Figure 4(B), device 10B includes an output unit 14 in addition to the configuration of device 10.

[0039] In this device 10A, the central processing unit 101 functions as a data acquisition unit 11, a prediction unit 12, and a detection unit 13. In this device 10B, the central processing unit 101 functions as a data acquisition unit 11, a prediction unit 12, and an output unit 14. The other hardware configuration is the same as in this device 10, except that the central processing unit in Figure 2 further includes at least one of the detection unit 13 and the output unit 14.

[0040] Next, another example of the processing of the organizational exodus prediction support program described herein will be specifically explained with reference to Figure 5. Figure 5 is a flowchart showing another example of each step of the organizational exodus prediction support program described herein.

[0041] First, the case including the detection unit 13 will be explained with reference to Figure 5(A). The data acquisition unit 11 further acquires, for example, location information data of a person (S11A, data acquisition procedure). The location information data may be acquired by means of, for example, GPS (Global Positioning System), Wi-Fi location information, Bluetooth beacon, IP address, various sensors, QR code (registered trademark), and NFC (Near Field Communication) tag.

[0042] Next, the detection unit 13 detects, for example, information on the person's behavior and information on the relationships between the people based on the location information data (S13, detection procedure). The behavior information may include, for example, records of movement between locations, attendance records, and participation records. The relationships between the people may include, for example, friendships, number of friends, level of intimacy, frequency of contact, and social network.

[0043] Subsequently, the prediction unit 12 predicts, for example, the likelihood that the person will leave a particular organization based on the acquired data and the detected information (S12A, prediction procedure).

[0044] Next, we will explain the case including the output unit 14 with reference to Figure 5(B).

[0045] The data acquisition unit 11 and the prediction unit 12 perform the same processing as described above (S11, S12).

[0046] Subsequently, the output unit 14 outputs, for example, the prediction result (S14, output procedure). The output is not particularly limited, but may be, for example, an output by the output device 106 of the present disclosure, or an output by another device. Examples of the output include text, numerical data, tables, audio, video, databases, and reports. The output unit 14 may further output at least one of the acquired data and the detected data.

[0047] Furthermore, this disclosure may also include both a detection unit 13 and an output unit 14, in addition to the data acquisition unit 11 and the prediction unit 12.

[0048] Next, the organizational exodus prediction support method of this disclosure will be further explained. The organizational exodus prediction support method of this disclosure can be implemented by referring to the descriptions in the organizational exodus prediction support program and organizational exodus prediction support device of this disclosure. The organizational exodus prediction support method of this disclosure is a method implemented by, for example, replacing each "procedure" in the program of this disclosure with "process". Specifically, the organizational exodus prediction support method of this disclosure further includes at least one of a detection process and an output process. The organizational exodus prediction support method of this disclosure can be implemented, for example, using at least one of the devices 10A and 10B in Figure 4. However, the organizational exodus prediction support method of this disclosure is not limited to the use of at least one of the devices 10A and 10B in Figure 4.

[0049] [Embodiment 3] This disclosure describes the trained model creation program, trained model creation device, and trained model creation method.

[0050] The trained model creation program disclosed herein is a program that causes a computer to execute a trained model creation procedure. It can also be described as a program that causes a computer to function as a trained model creation procedure. Furthermore, it can be described as a program that causes a computer to execute each step of the trained model creation method described later.

[0051] Next, an example of the trained model creation device described herein will be explained with reference to Figures 6 and 7.

[0052] Figure 6 is a block diagram showing the configuration of an example of the trained model creation device 20 (the device 20) of this disclosure. As shown in Figure 6, the device 20 includes a trained model creation unit 21.

[0053] The device 20 may be, for example, a single device including the aforementioned parts, or it may be a device in which the aforementioned parts can be connected via a communication network. Furthermore, the device 20 can be connected to external devices described later via the communication network. The communication network is not particularly limited and can use any known network, for example, it may be wired or wireless. Examples of the communication network include the Internet, WWW (World Wide Web), telephone lines, LAN (Local Area Network), SAN (Storage Area Network), DTN (Delay Tolerant Networking), LPWA (Low Power Wide Area), L5G (Local 5G), etc. Examples of wireless communication include Wi-Fi (registered trademark), Bluetooth (registered trademark), Local 5G, LPWA, etc. The wireless communication may be in the form of direct communication between devices (Ad Hoc communication), infrastructure communication, indirect communication via an access point, etc. The device 20 may be, for example, incorporated into a server as a system. Furthermore, the device 20 may be, for example, a personal computer (PC, e.g., desktop or notebook), smartphone, tablet terminal, digital signage, etc., on which the program of this disclosure is installed. The device 20 may also be in the form of cloud computing or edge computing, for example, in which at least one of the aforementioned parts is on a server and the other aforementioned parts are on a terminal.

[0054] Figure 7 illustrates a block diagram of the hardware configuration of the device 20. The device 20 includes, for example, a central processing unit (CPU, GPU, etc.) 201, memory 202, bus 203, storage device 204, input device 205, output device 206, communication device 207, etc. Each part of the device 20 is interconnected via the bus 203 through its respective interface (I / F).

[0055] The central processing unit 201 operates in coordination with other components via controllers (system controller, I / O controller, etc.) and is responsible for the overall control of the device 20. In the device 20, the central processing unit 201 executes, for example, the program disclosed herein and other programs, and also reads and writes various types of information. Specifically, for example, the central processing unit 201 functions as a trained model creation unit 21. The central processing unit 201 may be equipped with a CPU, GPU (Graphics Processing Unit), APU (Accelerated Processing Unit), or a combination thereof as its computing device.

[0056] Bus 203 can also be connected to external devices, for example. Examples of such external devices include external storage devices such as external databases, printers, external input devices, external display devices, and external imaging devices. The device 20 can be connected to an external network (the aforementioned communication network) by a communication device 207 connected to bus 203, for example, and can also be connected to other devices via the external network.

[0057] Memory 202 may be, for example, main memory. When the central processing unit 201 performs processing, memory 202 reads various operational programs, such as the program of this disclosure, stored in the storage device 204 (described later), and the central processing unit 201 receives data from memory 202 and executes the program. The main memory may be, for example, RAM (random access memory). Alternatively, memory 202 may be, for example, ROM (read-only memory).

[0058] The storage device 204 is also called a so-called auxiliary storage device, for example, in relation to the main memory (primary memory). As described above, the storage device 204 stores an operating program including the program of this disclosure. The storage device 204 may be, for example, a combination of a recording medium and a drive for reading from and writing to the recording medium. The recording medium is not particularly limited and may be internal or external, for example, an HD (hard disk), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, memory card, etc. The storage device 204 may be, for example, a hard disk drive (HDD) in which the recording medium and the drive are integrated, or a solid state drive (SSD).

[0059] In this device 20, the memory 202 and storage device 204 can also store various types of information, such as log information, information obtained from an external database (not shown) or external devices, information generated by this device 20, and information used by this device 20 when executing processing. In this case, the memory 202 and storage device 204 may store, for example, training data as described later. At least some of the information may be stored on an external server other than the memory 202 and storage device 204, or it may be stored in a distributed manner across multiple terminals using blockchain technology or the like.

[0060] The device 20 further includes, for example, an input device 205 and an output device 206. The input device 205 may include, for example, a pointing device such as a touch panel, trackpad, or mouse; a keyboard; imaging means such as a camera or scanner; a card reader such as an IC card reader or magnetic card reader; an audio input means such as a microphone; and so on. The output device 206 may include, for example, a display device such as an LED display or liquid crystal display; an audio output device such as a speaker; a printer; and so on. In this disclosure, the input device 205 and the output device 206 are configured separately, but the input device 205 and the output device 206 may be configured as an integrated unit, such as a touch panel display.

[0061] First, an example of the processing of the pre-trained model creation program of this disclosure will be specifically explained based on Figure 8. Figure 8 is a flowchart showing an example of each step of the pre-trained model creation program of this disclosure.

[0062] The trained model creation unit 21 learns based on training data including person identification data and the person's personal data, and when data including the identification data and the personal data is input, it creates a trained model that outputs a prediction result of the likelihood that the person will leave a specific organization (S21, trained model creation procedure). The identification data includes data acquired by the imaging means.

[0063] According to the pre-trained model creation program of this disclosure, the pre-trained model creation procedure creates a pre-trained model that learns based on training data including person identification data and the person's personal data, and when the data including the identification data and personal data is input, it outputs a prediction result of the likelihood that the person will leave a particular organization. This makes it possible to create a pre-trained model that can output information for predicting the likelihood that a person will leave a particular organization.

[0064] Next, the method for creating a trained model according to this disclosure will be described. The method for creating a trained model according to this disclosure can be implemented by referring to the descriptions in the trained model creation program and trained model creation apparatus according to this disclosure. The method for creating a trained model according to this disclosure is a method that is implemented by, for example, replacing each "procedure" in the program according to this disclosure with a "process". Specifically, the method for creating a trained model according to this disclosure includes a trained model creation process. The method for creating a trained model according to this disclosure can be implemented, for example, using the apparatus 20 in Figure 6. However, the method for creating a trained model according to this disclosure is not limited to the use of the apparatus 20 in Figure 6.

[0065] [Embodiment 4] Figure 9 illustrates an example of a method for supporting the prediction of the likelihood of leaving a particular organization using the organizational departure prediction support device described in this disclosure. While this disclosure shows examples using device 10, 10A, or 10B, it is not limited to these. Furthermore, while this disclosure uses the example of a student dropping out of school as an example of a person leaving a specific organization, it is not limited to this.

[0066] First, the device 10 acquires data including person identification data and the person's personal data. In Figure 9, for example, a student's identification data (ID) is acquired as identification data by the imaging means (the camera on the smartphone). In Figure 9, identification data is acquired by performing facial recognition on the student. Also in Figure 9, the student's personal data is acquired using the camera. For example, as physical information, the heart rate is acquired by the camera, as activity history information, attendance status to lectures linked to the ID is acquired, and as state information, the concentration level is acquired by the camera. The acquisition of the identification data and personal data by the imaging means is performed at a time instructed by, for example, a teacher, but is not limited to this and may be performed at any time.

[0067] Subsequently, the device 10 predicts the likelihood of a student dropping out of school based on the acquired data shown in the table in Figure 9. This prediction can be made, for example, using a trained model.

[0068] Furthermore, the device 10 may acquire location data of the students and detect information such as student behavior and relationships between students based on the location data. In this case, for example, based on the Wi-Fi connection information of the router installed in the classroom and the smartphone used by the student, the device 10 can detect behavioral information such as which classroom the student was in and which classes they attended, and can also detect student friendships based on the behavioral information of multiple students. The device 10 may predict the likelihood of a student dropping out of school based on the acquired data and the detected information.

[0069] The device 10 may further output the prediction results in any method and format.

[0070] [Embodiment 5] The program of this disclosure may be recorded on, for example, a computer-readable storage medium. The storage medium is, for example, a non-transitory computer-readable storage medium. The storage medium is not particularly limited and includes, for example, random access memory (RAM), read-only memory (ROM), hard disk (HD), flash memory (e.g., USB flash memory, SD / SDHC card, etc.), optical disc (e.g., CD-R / CD-RW, DVD-R / DVD-RW, BD-R / BD-RE, etc.), magneto-optical disk (MO), floppy disk (FD), etc. The program of this disclosure (for example, also referred to as a programming product or program product) may also be delivered, for example, from an external computer. The “delivery” may be, for example, delivered via a communication network or delivered via a wired device. The program of this disclosure may be installed and executed on the delivered device, or it may be executed without being installed.

[0071] Although the present disclosure has been described above with reference to embodiments, the present disclosure is not limited to the embodiments described above. The configuration and conditions of the present disclosure can be modified in various ways that can be understood by those skilled in the art within the scope of the present disclosure.

[0072] <Note> Some or all of the above embodiments may be described as follows, but are not limited to the following: (Note 1) Including data acquisition procedures and prediction procedures, The aforementioned data acquisition procedure acquires data including identification data of a person and personal data of the person, The aforementioned identification data includes data acquired by the imaging means, The aforementioned prediction procedure predicts the likelihood that the person will leave a particular organization based on the acquired data. A program to support the prediction of organizational departures, which causes a computer to perform each of the above steps. (Note 2) Furthermore, including detection procedures, The aforementioned data acquisition procedure further acquires location information data of the person, The detection procedure detects at least one of the person's behavioral information and the relationship between the people based on the location information data. The prediction procedure predicts the likelihood that the person will leave a particular organization based on the acquired data and the detected information. Organizational exodus prediction support program as described in Appendix 1. (Note 3) The aforementioned personal data includes information relating to at least one of the following: physical characteristics, activity history, and condition. Organizational withdrawal prediction support program as described in Appendix 1 or 2. (Note 4) The aforementioned personal data includes data acquired by the imaging means, Organizational exodus prediction support program as described in any of the appendices 1 to 3. (Note 5) The aforementioned specific organization is a school or a company, The aforementioned withdrawal is either dropping out of school or resigning. Organizational exodus prediction support program as described in any of the appendices 1 to 4. (Note 6) The aforementioned prediction procedure is a prediction using a trained model, The trained model is a trained model that learns based on training data including the identification data and the personal data, and when the data including the identification data and the personal data is input, it outputs a prediction result of the likelihood that the person will leave a particular organization. Organizational exodus prediction support program as described in any of Appendix 1 to 5. (Note 7) Furthermore, including the output procedure, The output procedure outputs the prediction result. Organizational exodus prediction support program as described in any of the appendices 1 to 6. (Note 8) The output procedure further outputs the acquired data. Organizational exodus prediction support program as described in Appendix 7. (Note 9) Includes the procedure for creating a pre-trained model, The aforementioned pre-trained model creation procedure creates a pre-trained model that learns based on training data including person identification data and the person's personal data, and when the data including the identification data and the personal data is input, it outputs a prediction result of the likelihood that the person will leave a particular organization. The aforementioned identification data includes data acquired by the imaging means, A program for creating pre-trained models. (Note 10) Including a data acquisition unit and a prediction unit, The data acquisition unit acquires data including identification data of a person and personal data of the person. The aforementioned identification data includes data acquired by the imaging means, The prediction unit predicts, based on the acquired data, the likelihood that the person will leave a particular organization. Organizational employee departure prediction support device. (Note 11) Furthermore, including a detection unit, The data acquisition unit further acquires location information data of the person, The detection unit detects, based on the location information data, at least one of the person's behavioral information and the relationship between the people, The prediction unit predicts the likelihood that the person will leave a particular organization based on the acquired data and the detected information. Organizational withdrawal prediction support device as described in Appendix 10. (Note 12) The aforementioned personal data includes information relating to at least one of the following: physical characteristics, activity history, and condition. A device for predicting organizational withdrawal as described in Appendix 10 or 11. (Note 13) The aforementioned personal data includes data acquired by the imaging means, A device for predicting organizational withdrawal, as described in any of Appendix 10 to 12. (Note 14) The aforementioned specific organization is a school or a company, The aforementioned withdrawal is either dropping out of school or resigning. A device for predicting organizational withdrawal, as described in any of the appendices 10 to 13. (Note 15) The aforementioned prediction unit makes predictions using a pre-trained model, The trained model is a trained model that learns based on training data including the identification data and the personal data, and when the data including the identification data and the personal data is input, it outputs a prediction result of the likelihood that the person will leave a particular organization. A device for predicting organizational withdrawal, as described in any of the appendices 10 to 14. (Note 16) Furthermore, including the output section, The output unit outputs the prediction result. A device for supporting the prediction of organizational withdrawal, as described in any of the appendices 10 to 15. (Note 17) The output unit further outputs the acquired data. Organizational withdrawal prediction support device as described in Appendix 16. (Note 18) Includes the pre-trained model creation section, The trained model creation unit trains on training data including person identification data and the person's personal data, and when data including the identification data and the personal data is input, it creates a trained model that outputs a prediction result of the likelihood that the person will leave a particular organization. The aforementioned identification data includes data acquired by the imaging means, A device for creating pre-trained models. (Note 19) Including a data acquisition process and a prediction process, The data acquisition step acquires data including identification data of a person and personal data of the person. The aforementioned identification data includes data acquired by the imaging means, The prediction step predicts the likelihood that the person will leave a particular organization based on the acquired data. A method for supporting organizational departure prediction, wherein each of the aforementioned steps is performed by a computer. (Note 20) Furthermore, including a detection process, The data acquisition step further acquires location information data of the person, The detection step detects, based on the location information data, at least one of the person's behavioral information and the relationship between the people, The prediction step predicts the likelihood that the person will leave a particular organization based on the acquired data and the detected information. The method for supporting prediction of organizational departure, as described in Appendix 19. (Note 21) The aforementioned personal data includes information relating to at least one of the following: physical characteristics, activity history, and condition. Organizational withdrawal prediction support method as described in Appendix 19 or 20. (Note 22) The aforementioned personal data includes data acquired by the imaging means, A method for supporting the prediction of organizational departure, as described in any of Appendix 19 to 21. (Note 23) The aforementioned specific organization is a school or a company, The aforementioned withdrawal is either dropping out of school or resigning. A method for supporting the prediction of organizational departure, as described in any of Appendix 19 to 22. (Note 24) The aforementioned prediction process is a prediction using a trained model, The trained model is a trained model that learns based on training data including the identification data and the personal data, and when the data including the identification data and the personal data is input, it outputs a prediction result of the likelihood that the person will leave a particular organization. A method for supporting the prediction of organizational departure, as described in any of Appendix 19 to 23. (Note 25) Furthermore, including the output process, The output step outputs the prediction result. A method for supporting the prediction of organizational departure, as described in any of Appendix 19 to 24. (Note 26) The output step further outputs the acquired data. The method for supporting prediction of organizational departure, as described in Appendix 25. (Note 27) Including the process of creating a pre-trained model, The pre-trained model creation step involves creating a pre-trained model that learns based on training data including person identification data and the person's personal data, and when the data including the identification data and the personal data is input, it outputs a prediction result of the likelihood that the person will leave a particular organization. The aforementioned identification data includes data acquired by the imaging means, A method for creating a trained model, wherein each of the above steps is performed by a computer. (Note 28) Including data acquisition procedures and prediction procedures, The aforementioned data acquisition procedure acquires data including identification data of a person and personal data of the person, The aforementioned identification data includes data acquired by the imaging means, The aforementioned prediction procedure predicts the likelihood that the person will leave a particular organization based on the acquired data. A computer-readable recording medium containing a program for supporting organizational departure prediction, which causes a computer to perform each of the aforementioned steps. (Note 29) Furthermore, including detection procedures, The aforementioned data acquisition procedure further acquires location information data of the person, The detection procedure detects at least one of the person's behavioral information and the relationship between the people based on the location information data. The prediction procedure predicts the likelihood that the person will leave a particular organization based on the acquired data and the detected information. Recording medium as described in Appendix 28. (Note 30) The aforementioned personal data includes information relating to at least one of the following: physical characteristics, activity history, and condition. Recording media as described in Appendix 28 or 29. (Note 31) The aforementioned personal data includes data acquired by the imaging means, A recording medium as described in any of the appendices 28 to 30. (Note 32) The aforementioned specific organization is a school or a company, The aforementioned withdrawal is either dropping out of school or resigning. A recording medium as described in any of the appendices 28 to 31. (Note 33) The aforementioned prediction procedure is a prediction using a trained model, The trained model is a trained model that learns based on training data including the identification data and the personal data, and when the data including the identification data and the personal data is input, it outputs a prediction result of the likelihood that the person will leave a particular organization. A recording medium as described in any of the appendices 28 to 32. (Note 34) Furthermore, including the output procedure, The output procedure outputs the prediction result. A recording medium as described in any of the appendices 28 to 33. (Note 35) The output procedure further outputs the acquired data. Recording medium as described in Appendix 34. (Note 36) Includes the procedure for creating a pre-trained model, The aforementioned pre-trained model creation procedure creates a pre-trained model that learns based on training data including person identification data and the person's personal data, and when the data including the identification data and the personal data is input, it outputs a prediction result of the likelihood that the person will leave a particular organization. The aforementioned identification data includes data acquired by the imaging means, A computer-readable recording medium containing a trained model creation program for causing a computer to perform each of the aforementioned steps. [Industrial applicability]

[0073] This disclosure provides an organization departure prediction support program, a trained model creation program, an organization departure prediction support device, an organization departure prediction support method, and a recording medium for supporting the prediction of the likelihood of leaving a certain organization using biometric authentication technology. The fields to which this disclosure can be applied are not limited, and the program described herein is useful in various fields. [Explanation of Symbols]

[0074] 10, 10A, 10B Tissue Depletion Prediction Support Device 11 Data Acquisition Unit 12 Prediction Section 13 Detection unit 14 Output section 20. Pre-trained model creation device 21. Pre-trained model creation section 101, 201 CPU 102, 202 memory Buses 103 and 203 104, 204 Storage device 105, 205 Input devices 106, 206 Output devices 107, 207 Communication devices

Claims

1. Including data acquisition procedures and prediction procedures, The aforementioned data acquisition procedure acquires data including identification data of a person and personal data of the person, The aforementioned identification data includes data acquired by the imaging means, The aforementioned prediction procedure predicts the likelihood that the person will leave a particular organization based on the acquired data. A program to support the prediction of organizational departures, which causes a computer to perform each of the above steps.

2. Furthermore, including detection procedures, The aforementioned data acquisition procedure further acquires location information data of the person, The detection procedure detects at least one of the person's behavioral information and the relationship between the people based on the location information data. The prediction procedure predicts the likelihood that the person will leave a particular organization based on the acquired data and the detected information. The organizational departure prediction support program according to claim 1.

3. The aforementioned personal data includes information relating to at least one of the following: physical characteristics, activity history, and condition. The organizational departure prediction support program according to claim 1 or 2.

4. The aforementioned personal data includes data acquired by the imaging means, The organizational departure prediction support program according to claim 1 or 2.

5. The aforementioned specific organization is a school or a company, The aforementioned withdrawal is either dropping out of school or resigning. The organizational departure prediction support program according to claim 1 or 2.

6. The aforementioned prediction procedure is a prediction using a trained model, The trained model is a trained model that learns based on training data including the identification data and the personal data, and when the data including the identification data and the personal data is input, it outputs a prediction result of the likelihood that the person will leave a particular organization. The organizational departure prediction support program according to claim 1 or 2.

7. Includes the procedure for creating a pre-trained model, The aforementioned pre-trained model creation procedure creates a pre-trained model that learns based on training data including person identification data and the person's personal data, and when the data including the identification data and the personal data is input, it outputs a prediction result of the likelihood that the person will leave a particular organization. The aforementioned identification data includes data acquired by the imaging means, A program for creating pre-trained models.

8. Including a data acquisition unit and a prediction unit, The data acquisition unit acquires data including identification data of a person and personal data of the person. The aforementioned identification data includes data acquired by the imaging means, The prediction unit predicts, based on the acquired data, the likelihood that the person will leave a particular organization. Organizational employee departure prediction support device.

9. Including a data acquisition process and a prediction process, The data acquisition step acquires data including identification data of a person and personal data of the person. The aforementioned identification data includes data acquired by the imaging means, The prediction step predicts the likelihood that the person will leave a particular organization based on the acquired data. A method for supporting organizational departure prediction, wherein each of the aforementioned steps is performed by a computer.

10. Including data acquisition procedures and prediction procedures, The aforementioned data acquisition procedure acquires data including identification data of a person and personal data of the person, The aforementioned identification data includes data acquired by the imaging means, The aforementioned prediction procedure predicts the likelihood that the person will leave a particular organization based on the acquired data. A computer-readable recording medium containing a program for supporting organizational departure prediction, which causes a computer to perform each of the aforementioned steps.

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