JP7578266B2 - Selection device and program - Google Patents

Description

The present invention relates to a selection device and a program.

Online interviews using networks have traditionally been conducted. Online interviews can be conducted without the need to go to an interview venue, saving time for applicants and businesses (companies) and eliminating the need to secure a venue. As a device capable of conducting such online interviews, a device capable of sending and receiving questions and answers to a terminal carried by an applicant has been proposed (see, for example, Patent Document 1).

JP 2003-263537 A

When interviewing a large number of candidates for new graduate recruitment etc., it was necessary to narrow down the applicants from thousands to tens of thousands of applicants. As a result, it took a lot of time and effort to decide on the successful candidates. Furthermore, businesses wish to hire applicants who are more in line with their desired personnel profile. For example, it is desirable to hire applicants who fit the corporate culture, rather than simply those who performed well on exams. In addition, when using video provided by applicants online, there was the problem that the selection process took a long time because it was necessary to view all of the video.

The present invention was made in consideration of the above-mentioned conventional situation, and aims to provide a selection device and program that makes it easier to decide on successful applicants and can identify applicants who better meet the wishes of the business operator.

The present invention relates to a selection device that selects applicants for a specified business based on video images containing the applicants as subjects, the selection device comprising: a video image acquisition unit that acquires the video images; a recommendation level judgment unit that judges the pass recommendation level of the applicant based on the features of the applicant contained in the video images, video images containing other applicants as subjects, and information regarding the pass/fail judgments of the other applicants; and an output unit that outputs the pass recommendation level of the applicant based on the judgment result.

It is also preferable that the selection device further includes an extraction unit that extracts features of the applicant contained in the video.

It is also preferable that the extraction unit labels the applicant based on the extracted features, and the recommendation level determination unit determines the recommendation level for passing the applicant based on the label attached to the applicant, the labels attached to the other applicants, and information regarding the pass/fail judgments of the other applicants.

In addition, the selection device preferably further includes an evaluation unit that evaluates the tendency of the judgment results regarding the pass recommendation level, and an adjustment unit that adjusts the feature amount to be emphasized based on the evaluation results, and the recommendation level judgment unit preferably judges the pass recommendation level using the adjusted feature amount.

It is also preferable that the extraction unit extracts at least one feature of the applicant's clothing, facial expression, tone of voice, ambient lighting, room background, facial features, body type, hairstyle, and shooting environment contained in the video.

It is also preferable that the recommendation level determination unit determines the recommendation level for passing the applicant based on labels attached to video images of past successful applicants.

In addition, it is preferable that the recommendation level determination unit determines the recommendation level for passing the applicant based on features related to at least one of the logic of the successful applicant's responses, speaking style, behavior, statements, and the sincerity of the contents of the statements.

It is also preferable that the recommendation level determination unit further determines the recommendation level for passing based on information regarding the applicant's status and external evaluation factors.

It is also preferable that the selection device further includes a judgment unit that judges whether or not the applicant who is judged to have a predetermined or higher degree of pass recommendation exceeds the pass mark.

It is also preferable that the selection device further includes a satisfaction level calculation unit that calculates the satisfaction level of the applicant with respect to the label.

In addition, it is preferable that the selection device further includes a simulation execution unit that uses the acquired video image to simulate a pass/fail decision for the specified business operator, and the output unit outputs the simulation result.

It is also preferable that the selection device further includes an analysis unit that analyzes the tendencies of the determined successful applicants.

In addition, it is preferable that the selection device further includes an analysis unit that analyzes the tendencies of applicants who are determined not to have passed the pass mark.

It is also preferable that the extraction unit sorts the acquired multiple video images using a predetermined feature amount.

The present invention also relates to a selection device that selects applicants for a specified business based on video images that include the applicants as subjects, the selection device comprising a video image acquisition unit that acquires the video images, an extraction unit that extracts features of the applicants included in the video images, and an output unit that outputs the extracted features.

The present invention also relates to a program that causes a computer to function as a selection device that selects applicants to a specified business based on video images that include the applicants as subjects, and causes the computer to function as a video image acquisition unit that acquires the video images, a recommendation level determination unit that determines the pass recommendation level of the applicant based on the features of the applicant included in the video images, video images that include other applicants as subjects, and information regarding the pass/fail judgments of the other applicants, and an output unit that outputs the pass recommendation level of the applicant based on the judgment results.

This disclosure makes it possible to provide a selection device and program that makes it easier to determine successful applicants and can identify applicants who better meet the operator's preferences.

1 is a schematic diagram showing a system configuration of a selection system including a selection device according to one embodiment of the present invention. 1 is a block diagram showing a configuration of a selection device according to an embodiment; 13 is a screen diagram showing a result determined by a recommendation level determination unit of the selection device according to one embodiment. FIG. 13 is an example of a screen diagram showing a result of evaluation by an evaluation unit of the selection device of the embodiment; 13 is another example of a screen diagram showing a result of evaluation by the evaluation unit of the selection device of the embodiment. 10 is a flowchart illustrating an operation of the selection device according to an embodiment.

A selection device 1 and a program according to an embodiment of the present invention will be described below with reference to FIGS. 1 to 6. FIG.
First, a selection system 100 including a selection device 1 according to this embodiment will be described.

The selection system 100 is, for example, a system that determines the recommendation level of an applicant U who has applied for a job offer from a business operator (company). The selection system 100 determines the recommendation level of the applicant U based on, for example, video images provided by the applicant U. In particular, the selection system 100 determines a high recommendation level for an applicant U who is considered to fit the business operator's corporate culture. This selection system 100 is a system that is used, for example, in the document screening stage before the first interview. The selection system 100 includes a user terminal 20 and a selection device 1.

The user terminal 20 is, for example, a mobile terminal such as a smartphone or a personal computer. The user terminal 20 is configured to be able to capture moving images of the applicant U. The user terminal 20 captures moving images of the user's responses to tasks (such as speeches) provided in advance, for example.

The selection device 1 is, for example, a server. The selection device 1 is connected to a user terminal 20 via, for example, an Internet line N. The selection device 1 is configured to be able to acquire video images captured by the user terminal 20. In other words, the selection device 1 is configured to be able to acquire video images of applicants U applying to a specified business. The selection device 1 selects applicants U based on video images that include applicants U as subjects for the specified business.

Next, the configuration of the selection device 1 according to this embodiment will be described with reference to FIG.
The selection device 1 includes a video acquisition unit 101, a video storage unit 102, an extraction unit 103, a label storage unit 104, a feature storage unit 105, a recommendation level judgment unit 106, a judgment result storage unit 107, an evaluation unit 108, an adjustment unit 109, a judgment unit 110, a satisfaction level calculation unit 111, an analysis unit 112, and an output unit 113.

The video image acquisition unit 101 is realized, for example, by the operation of a CPU. The video image acquisition unit 101 acquires video images transmitted from the user terminal 20.

The video storage unit 102 is, for example, a recording medium such as a hard disk. The video storage unit 102 stores the acquired video.

The extraction unit 103 is realized, for example, by the operation of a CPU. The extraction unit 103 extracts feature amounts of the applicant U contained in the video. The extraction unit 103 extracts, for example, at least one feature amount of the applicant U's clothing, facial expression, tone of voice, ambient lighting, room background, facial features, body type, hairstyle, and shooting environment contained in the video. The extraction unit 103 also labels the applicant U based on the extracted feature amounts.

The label storage unit 104 is, for example, a recording medium such as a hard disk. The label storage unit 104 stores, for example, labels included in video images that include the past applicant U as a subject, and information regarding the pass/fail judgment of the past applicant U.

The feature storage unit 105 is, for example, a recording medium such as a hard disk. The feature storage unit 105 stores feature amounts (labels) that are emphasized according to the business operator. The feature storage unit 105 stores, for example, the amount of weighting for the feature amounts (labels). For example, when emphasis is placed on the tone of voice feature, the feature storage unit 105 stores a weighting amount that gives more weight to the tone of voice feature.

The recommendation level determination unit 106 is realized, for example, by the operation of a CPU. The recommendation level determination unit 106 determines the pass recommendation level of the applicant U based on the feature amount of the applicant U included in the video, the video including other applicants U as subjects, and information on the pass/fail judgments of the past applicants U. Specifically, the recommendation level determination unit 106 determines the pass recommendation level of the applicant U based on the label attached to the applicant U, the label attached to the past applicants U, and information on the pass/fail judgments of the past applicants U. That is, the recommendation level determination unit 106 determines the pass recommendation level of the applicant U based on the label attached to the video of the past successful applicants. In addition, the recommendation level determination unit 106 determines the pass recommendation level of the applicant U based on the feature amount related to at least one of the logic of the successful applicant's response, the way of speaking, behavior, remarks, and the sincerity of the remarks. The recommendation level determination unit 106 determines the pass recommendation level using, for example, attractiveness, clarity, fluency, and smile level as feature values, as shown in FIG. 3. The recommendation level determination unit 106 displays the pass recommendation level in percentage. The recommendation level determination unit 106 also determines the pass recommendation level using the feature values adjusted by the adjustment unit 109, which will be described later.

The judgment result storage unit 107 is, for example, a recording medium such as a hard disk. The judgment result storage unit 107 stores the pass recommendation level determined by the recommendation level determination unit 106 as the judgment result. The judgment result storage unit 107 stores, for example, the applicant U and the corresponding pass recommendation level as the judgment result.

The evaluation unit 108 is realized, for example, by the operation of a CPU. The evaluation unit 108 evaluates the tendency of the judgment results regarding the degree of recommendation for passing. For example, the evaluation unit 108 evaluates the tendency of whether the judgment results of multiple applicants U include many applicants U with high or low specific characteristic amounts. Specifically, the evaluation unit 108 evaluates the tendency of whether the judgment results of multiple applicants U include many applicants U with high attractiveness levels. For example, as shown in FIG. 4, the evaluation unit 108 evaluates the tendency of the characteristic amounts of all applicants U included in the judgment results.

The adjustment unit 109 is realized, for example, by the operation of a CPU. The adjustment unit 109 adjusts the feature amount to be emphasized based on the evaluation result. The adjustment unit 109, for example, performs an adjustment to increase or decrease the weighting of a specific feature amount. The adjustment unit 109, for example, externally acquires the feature amount to be adjusted and the adjustment amount for the feature amount included in the screen diagram shown in FIG. 4. The adjustment unit 109 adjusts the weighting based on the acquired feature amount and adjustment amount. For example, when it is desired to increase the pass recommendation level for applicant U who has strengths in attractiveness, the adjustment unit 109 performs an adjustment to increase the weighting for attractiveness.

The judgment unit 110 is realized, for example, by the operation of a CPU. The judgment unit 110 judges whether or not an applicant U who has been judged to have a predetermined or higher degree of recommendation for passing has exceeded the pass line. For example, the judgment unit 110 judges that an applicant U who meets a predetermined pass line has exceeded the pass line. The judgment unit 110 judges whether or not an applicant U has exceeded the pass line, for example, based on a pass line that is predetermined according to the number of applicants to be passed.

The fulfillment degree calculation unit 111 is realized, for example, by the operation of a CPU. The determination unit 110 calculates the fulfillment degree for the label for the applicant U. The fulfillment degree calculation unit 111 calculates the fulfillment degree of the label attached to each applicant U, for example, as shown in FIG. 5.

The analysis unit 112 is realized, for example, by the operation of a CPU. The analysis unit 112 analyzes the tendencies of the applicant U who has been determined to have exceeded the pass line. The analysis unit 112 analyzes, for example, the tendencies of the feature values of the applicant U who has exceeded the pass line.

The output unit 113 is realized, for example, by the operation of a CPU. The output unit 113 outputs the degree of recommendation for passing the applicant U based on the judgment result. The output unit 113 also outputs the evaluation result by the evaluation unit 108, the degree of satisfaction calculated by the satisfaction degree calculation unit 111, and the tendency of the feature amount analyzed by the analysis unit 112. The output unit 113 outputs, for example, to a display device (not shown) or the like.

Next, the operation of the selection device 1 will be described with reference to the flowchart of FIG.
First, the video acquisition unit 101 acquires a video from the user terminal 20 (step S1). Next, the extraction unit 103 extracts features contained in the video (step S2). Next, the recommendation level calculation unit calculates the pass recommendation level for each applicant U (step S3).

Next, it is determined whether or not the pass line needs to be determined (step S4). If the pass line needs to be determined (step S4: YES), the process proceeds to step S5. On the other hand, if the pass line does not need to be determined (step S4: NO), the process proceeds to step S7.

In step S5, the judgment unit 110 judges whether or not the applicant U exceeds a predetermined pass mark. Next, the satisfaction level calculation unit 111 calculates the satisfaction level (step S6). Next, the process proceeds to step S7.

In step S7, it is determined whether or not the feature amount needs to be adjusted. If the feature amount needs to be adjusted (step S7: YES), the process proceeds to step S8. On the other hand, if the feature amount does not need to be adjusted (step S7: NO), the process proceeds to step S10.

In step S8, the evaluation unit 108 evaluates the result of the judgment of the pass recommendation level. Next, the adjustment unit 109 adjusts the weighting of the feature amount based on the adjustment amount obtained based on the judgment result (step S9). Next, the process proceeds to step S10.

In step S10, the output unit 113 outputs the judgment result. The output unit 113 also outputs the judgment result and the evaluation result as necessary. This completes the processing according to this flow.

Next, the program will be described.
Each component included in the selection device 1 can be realized by hardware, software, or a combination of these. Here, being realized by software means being realized by a computer reading and executing a program.

The program can be stored and supplied to the computer using various types of non-transitory computer readable media. Non-transitory computer readable media include various types of tangible storage media. Examples of non-transitory computer readable media include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R/Ws, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (random access memories)). The display program may also be supplied to the computer by various types of transitory computer readable media. Examples of transitory computer readable media include electrical signals, optical signals, and electromagnetic waves. The transitory computer readable media can supply the program to the computer via wired communication paths such as electric wires and optical fibers, or wireless communication paths.

As described above, the selection device 1 and the program according to the present embodiment provide the following advantages.
(1) A selection device 1 that selects an applicant U for a specified business based on a video image containing the applicant U as a subject, and includes a video image acquisition unit 101 that acquires the video image, a recommendation level judgment unit 106 that judges the recommendation level for passing the applicant U based on features of the applicant U contained in the video image, video images containing other applicants U as subjects, and information regarding the pass/fail judgment of the other applicants U, and an output unit 113 that outputs the recommendation level for passing the applicant U based on the judgment result.
The program also causes a computer to function as a selection device 1 that selects applicants U for a specified business based on video images containing the applicant U as a subject, and causes the computer to function as a video image acquisition unit 101 that acquires the video images, a recommendation level judgment unit 106 that judges the degree of recommendation for passing applicant U based on features of applicant U contained in the video images, video images containing other applicants U as subjects, and information regarding the pass/fail judgments of the other applicants U, and an output unit 113 that outputs the degree of recommendation for passing applicant U based on the judgment result.
This makes it possible to output the pass recommendation level for applicant U according to the operator's wishes, facilitating the process of deciding on successful applicants and enabling the operator to determine which applicant U best suits the operator's wishes.

(2) The selection device 1 further includes an extraction unit 103 that extracts features of the applicant U contained in the video. This makes it possible to easily obtain features used for judgment from the video.

(3) The extraction unit 103 labels the applicant U based on the extracted features, and the recommendation level determination unit 106 determines the recommendation level for passing the applicant U based on the label attached to the applicant U, the labels attached to the other applicants U, and information regarding the pass/fail decisions of the other applicants U. This makes it possible to determine the applicant U who is more suited to the operator's wishes based on the other applicants U.

(4) The selection device 1 further includes an evaluation unit 108 that evaluates the tendency of the judgment result regarding the pass recommendation level, and an adjustment unit 109 that adjusts the feature amount to be emphasized based on the evaluation result, and the recommendation level judgment unit 106 judges the pass recommendation level using the adjusted feature amount. This makes it possible to more appropriately adjust the pass recommendation level even if the pass recommendation level is biased.

(5) The recommendation level determination unit 106 determines the recommendation level for applicant U based on the labels attached to the video images of past successful applicants. This makes it possible to determine applicant U who is more suited to the operator's wishes.

(6) The selection device 1 further includes a judgment unit 110 that judges whether an applicant U who is judged to have a predetermined or higher pass recommendation level exceeds a pass line. This makes the selection process easier because it can also determine whether the applicant passes or fails.

(7) The selection device 1 further includes a satisfaction level calculation unit 111 that calculates the satisfaction level of the label for the applicant U. This makes it easy to obtain the reason for the judgment.

(8) The selection device 1 further includes an analysis unit 112 that analyzes the tendencies of the determined successful candidates. This makes it easy to determine whether or not the successful candidates actually meet the wishes of the business operator.

Although the above describes preferred embodiments of the selection device and program of the present invention, the present disclosure is not limited to the above-described embodiments and can be modified as appropriate.

For example, in the above embodiment, the recommendation level determination unit 106 determines the pass recommendation level based on past applicant U, but is not limited to this. The recommendation level determination unit 106 may determine the pass recommendation level of the actual applicant U by taking another applicant U as the ideal image of applicant U as envisioned by the business operator.

In addition, in the above embodiment, the selection device 1 is described as having the extraction unit 103, but is not limited to this. Instead of the extraction unit 103, a feature amount that has already been extracted from a video may be acquired. The recommendation level determination unit 106 may use the extracted feature amount to calculate the pass recommendation level.

In the above embodiment, the recommendation level determination unit 106 may further determine the pass recommendation level based on information related to the applicant U's status and external evaluation factors. The recommendation level determination unit 106 may determine the pass recommendation level based on, for example, information related to the applicant U's career and test results. The recommendation level determination unit 106 may use the results of a dark color test as the external evaluation factors.

In the above embodiment, the selection device 1 may also include a simulation execution unit 114 that uses the acquired video to simulate a pass/fail decision to apply to a specified business. The output unit 113 may output the simulation result. For example, the simulation execution unit 114 may simulate a pass/fail decision before actually applying to a business. The output unit 113 may transmit the simulation result to the user terminal 20 that transmitted the video.

In the above embodiment, the selection device 1 may acquire video images of the applicant U for each of the multiple businesses. The selection device 1 may also determine the degree of recommendation for passing for each of the multiple businesses. The selection device 1 may acquire the video images from the user terminal 20 and may also acquire the names of the businesses to which the applicant is applying.

In the above embodiment, the labels are not limited to those that are assigned based on features extracted from the video. For example, the labels may be adjusted and determined by creating a correct label from a predetermined standard and a plurality of videos, and then using the difference between the correct label and the standard when judging the other plurality of videos as the correct label.

In addition, in the above embodiment, the output unit 113 outputs the pass recommendation level for applicant U, but this is not limited to this. The output unit 113 may output the feature amount without outputting the pass recommendation level. Furthermore, the output unit 113 may output both the pass recommendation level and the feature amount.

In the above embodiment, the extraction unit 103 may extract attributes of the applicant U (such as athletics and international student) from the content included in the video. The recommendation level determination unit 106 may determine the recommendation level for passing based on the attributes.

In the above embodiment, the simulation execution unit 114 may make a judgment by combining information other than video images (meta information (information on proficiency tests and academic records, etc.)). The pass/fail judgment by the simulation execution unit 114 is not limited to a binary judgment, but may be classified into three or more values. The pass/fail judgment by the simulation execution unit 114 may be indicated by a continuous value, such as a pass rate.

In addition, in the above embodiment, the analysis unit 112 may analyze the tendencies of the applicant U who has been determined not to have passed the pass line.

In addition, in the above embodiment, the selection device 1 is not limited to the document screening stage before the first interview, but may be used at any stage in the selection process.

REFERENCE SIGNS LIST 1 Selection device 101 Video image acquisition unit 102 Video image storage unit 103 Extraction unit 106 Recommendation level determination unit 108 Evaluation unit 109 Adjustment unit 110 Judgment unit 111 Satisfaction level calculation unit 112 Analysis unit 113 Output unit 114 Simulation execution unit U Applicant

Claims (14)

1. A selection device for selecting an applicant for a predetermined business based on a moving image including the applicant as a subject, comprising:
A moving image acquisition unit that acquires the moving image;
a recommendation level determination unit that determines the recommendation level for passing the applicant in accordance with criteria based on a plurality of feature amounts of the applicant contained in the video, video images including other applicants as subjects, and information regarding the pass/fail decisions of the other applicants;
an output unit that outputs the pass recommendation level of the applicant based on the judgment result;
An evaluation unit that evaluates a tendency of a judgment result regarding the pass recommendation degree;
an adjustment unit that adjusts an important feature quantity among the plurality of feature quantities in the judgment criterion based on a result of the evaluation;
Equipped with
the recommendation level determination unit determines the pass recommendation level of the applicant using the adjusted feature amount.
Selection device. The selection device according to claim 1, further comprising an extraction unit that extracts the plurality of features of the applicant contained in the video. The selection device according to claim 2, wherein the extraction unit extracts the plurality of features including at least one feature of the applicant's clothing, facial expression, tone of voice, ambient lighting, room background, facial features, body type, hairstyle, and shooting environment contained in the video. The extraction unit performs labeling on the applicant based on the extracted plurality of feature amounts; and
The selection device described in claim 2 or 3, wherein the recommendation level determination unit determines the recommendation level for passing the applicant based on criteria based on the label attached to the applicant, the labels attached to the other applicants, and information regarding the pass/fail judgments of the other applicants. The selection device according to claim 4 , wherein the recommendation level determination unit determines the recommendation level for passing the applicant based on the judgment criteria that are further adjusted based on labels attached to videos of past successful applicants. The selection device according to claim 5, wherein the recommendation level determination unit further determines the recommendation level for passing the applicant based on at least one of the features of the successful applicant regarding the logic of the successful applicant's responses, the way of speaking, behavior, statements, and the sincerity of the contents of the statements. The selection device according to any one of claims 3 to 6, wherein the recommendation level determination unit further determines the pass recommendation level of the applicant based on information regarding the applicant's status and external evaluation factors. The selection device according to any one of claims 3 to 7, further comprising a determination unit that determines whether or not an applicant who is determined to have a predetermined or higher pass recommendation level exceeds a pass line. The selection device according to claim 4, further comprising a satisfaction degree calculation unit that calculates the satisfaction degree of the applicant with respect to the label. A simulation execution unit that simulates a pass/fail decision for a predetermined business using the acquired moving image,
The selection device according to claim 2 , wherein the output unit outputs a result of the simulation. The selection device according to claim 8, further comprising an analysis unit that analyzes the tendencies of the applicants who have been determined to have exceeded the pass line. The selection device according to claim 8, further comprising an analysis unit that analyzes the tendencies of the applicants who are determined not to have passed the pass line. The selection device according to any one of claims 2 to 12, wherein the extraction unit sorts the acquired multiple videos using a predetermined feature among the multiple feature amounts. A program that causes a computer to function as a selection device that selects applicants for a predetermined business based on a video image including the applicants as subjects,
The computer,
a moving image acquisition unit for acquiring the moving image;
a recommendation level determination unit that determines the recommendation level for passing the applicant in accordance with criteria based on a plurality of feature amounts of the applicant contained in the video, video images including other applicants as subjects, and information regarding the pass/fail decisions of the other applicants;
an output unit that outputs the pass recommendation level for the applicant based on the judgment result;
an adjustment unit that adjusts an important feature quantity among the plurality of feature quantities in the judgment criterion based on a result of the evaluation;
Functioning as a
The recommendation level determination unit is a unit for determining the pass recommendation level of the applicant by using the adjusted feature amount,
A program that makes it work.

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