Selection equipment and programs

Abstract

To provide a selection device and a program that are capable of facilitating a process to determine passers and determining an applicant who meets an employer's desire.SOLUTION: A selection device 1 for performing the selection of an applicant U on the basis of a dynamic image including the applicant U to a predetermined employer as a subject comprises: a dynamic image acquisition unit 101 for acquiring dynamic images; a recommendation degree determination unit 106 for determining a pass recommendation degree of the applicant U, on the basis of a feature quantity of the applicant U included in the dynamic image, a dynamic image including another applicant U as a subject, and information related to determination whether another applicant U is passed or not; and an output unit 113 for outputting the pass recommendation degree of the applicant U on the basis of a determination result.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a selection device and a program. [Background technology]

[0002] Online interviews using networks have been conducted for some time. Online interviews can be conducted without the need to physically visit 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). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2003-263537 Summary of the Invention [Problem to be solved by the invention]

[0004] When interviewing a large number of candidates, such as for new graduate recruitment, it is necessary to narrow down the applicants from thousands to tens of thousands of applicants. This requires a lot of time and effort to select the successful candidates. Furthermore, businesses want to hire applicants who are more suited to their desired talent. For example, they want to hire applicants who fit the company culture, not just those with good test scores. Furthermore, when using video provided by applicants online, the selection process takes a long time because all of the video must be viewed.

[0005] The present invention has been 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 determine applicants who better meet the wishes of the employer. [Means for solving the problem]

[0006] The present invention relates to a selection device that selects applicants for a specified business based on video images containing the applicants as subjects, and that includes a video image acquisition unit that acquires the video images, a recommendation level judgment unit that judges the degree of recommendation for passing 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 degree of recommendation for passing the applicant based on the judgment results.

[0007] Preferably, the selection device further comprises an extraction unit that extracts features of the applicant contained in the video.

[0008] 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 assigned to the applicant, the labels assigned to the other applicants, and information regarding the pass / fail decisions of the other applicants.

[0009] In addition, the selection device preferably further includes an evaluation unit that evaluates the tendency of the judgment results regarding the degree of recommendation for passing, and an adjustment unit that adjusts the feature quantities to be emphasized based on the evaluation results, and the recommendation degree judgment unit preferably judges the degree of recommendation for passing using the adjusted feature quantities.

[0010] 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.

[0011] 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.

[0012] 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 statements.

[0013] It is also preferable that the recommendation level determination unit determines the degree of recommendation for passing based on information regarding the applicant's status and external evaluation factors.

[0014] Preferably, the selection device further comprises a determination unit that determines whether or not the applicant who has been determined to have a predetermined or higher degree of recommendation for passing has exceeded the passing grade.

[0015] Preferably, the selection device further comprises a fulfillment level calculation unit that calculates the fulfillment level of the applicant with respect to the label.

[0016] Preferably, the selection device further comprises a simulation execution unit that simulates a pass / fail decision for the specified business using the acquired moving image, and the output unit outputs the simulation result.

[0017] Preferably, the selection device further comprises an analysis unit that analyzes the tendencies of the determined successful applicants.

[0018] Preferably, the selection device further comprises an analysis unit for analyzing the tendencies of applicants who have been determined not to have passed the pass mark.

[0019] It is also preferable that the extraction section sorts the plurality of acquired moving images using a predetermined feature amount.

[0020] The present invention also relates to a selection device that selects applicants for a specified business based on moving images containing the applicants as subjects, the selection device comprising: a moving image acquisition unit that acquires the moving images; an extraction unit that extracts features of the applicants contained in the moving images; and an output unit that outputs the extracted features.

[0021] The present invention also relates to a program that causes a computer to function as a selection device that selects applicants for a specified business based on video images containing the applicants as subjects, and that causes the computer to function as a video image acquisition unit that acquires the video images, a recommendation level judgment unit that judges the degree of recommendation for passing 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 degree of recommendation for passing the applicant based on the judgment results. [Effects of the Invention]

[0022] According to the present disclosure, it is possible to provide a selection device and program that makes it easier to decide on successful applicants and is capable of determining applicants who better meet the wishes of the business operator. [Brief explanation of the drawings]

[0023] [Figure 1] 1 is a schematic diagram showing the system configuration of a selection system including a selection device according to an embodiment of the present invention. [Figure 2] 1 is a block diagram showing a configuration of a selection device according to an embodiment; [Figure 3] 10 is a screen diagram showing the results determined by a recommendation level determination unit of the selection device of the embodiment. FIG. [Figure 4] 10 is an example of a screen diagram showing the results of evaluation by the evaluation unit of the selection device of the embodiment; [Figure 5] 10 is another example of a screen diagram showing the results of evaluation by the evaluation unit of the selection device of the embodiment. [Figure 6] 10 is a flowchart illustrating the operation of the selection device according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0024] A selection device 1 and a program according to one embodiment of the present invention will be described below with reference to FIGS. First, a selection system 100 including a selection device 1 according to this embodiment will be described.

[0025] The selection system 100 is a system that determines, for example, the recommendation level for applicant U who has applied for a job at a business (company). The selection system 100 determines the recommendation level for applicant U based on, for example, video images provided by applicant U. In particular, the selection system 100 determines a high recommendation level for applicant U who is thought to fit the business'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.

[0026] 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. For example, the user terminal 20 captures moving images of the user's responses to tasks (such as speeches) provided in advance.

[0027] 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 moving images captured by the user terminal 20. In other words, the selection device 1 is configured to be able to acquire moving images of applicants U applying to a specified business. The selection device 1 selects applicants U for the specified business based on moving images that include applicants U as subjects.

[0028] 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 determination unit 106, a determination 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.

[0029] The video acquisition unit 101 is realized by, for example, the operation of a CPU. The video acquisition unit 101 acquires video transmitted from the user terminal 20.

[0030] The video storage unit 102 is, for example, a recording medium such as a hard disk, etc. The video storage unit 102 stores the acquired video.

[0031] The extraction unit 103 is realized, for example, by the operation of a CPU. The extraction unit 103 extracts feature quantities of the applicant U contained in the video. For example, the extraction unit 103 extracts at least one feature quantity of the applicant U contained in the video, including clothing, facial expression, tone of voice, ambient lighting, room background, facial features, body type, hairstyle, and shooting environment. The extraction unit 103 also labels the applicant U based on the extracted feature quantities.

[0032] 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 moving images that include the past applicant U as a subject, and information regarding the pass / fail judgment of the past applicant U.

[0033] 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 depending on 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.

[0034] The recommendation level determination unit 106 is realized, for example, by the operation of a CPU. The recommendation level determination unit 106 determines the recommendation level for applicant U based on the feature values ​​of applicant U contained in the video, video images including other applicants U as subjects, and information regarding the pass / fail decisions of past applicants U. Specifically, the recommendation level determination unit 106 determines the recommendation level for applicant U based on the labels attached to applicant U, labels attached to past applicants U, and information regarding the pass / fail decisions of past applicants U. That is, 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. The recommendation level determination unit 106 also determines the recommendation level for applicant U based on feature values ​​related to at least one of the logic of the successful applicant's responses, speaking style, behavior, statements, and the sincerity of the statements. The recommendation level determination unit 106 determines the pass recommendation level using, for example, attractiveness, clarity, fluency, and smile level as feature quantities, as shown in Fig. 3. The recommendation level determination unit 106 displays the pass recommendation level as a percentage. The recommendation level determination unit 106 also determines the pass recommendation level using feature quantities adjusted by the adjustment unit 109, which will be described later.

[0035] 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, applicant U and the corresponding pass recommendation level as the judgment result.

[0036] The evaluation unit 108 is realized, for example, by the operation of a CPU. The evaluation unit 108 evaluates the trend of the judgment results regarding the degree of recommendation for passing. For example, the evaluation unit 108 evaluates the trend of whether the judgment results for multiple applicants U include many applicants U with high or low specific feature amounts. Specifically, the evaluation unit 108 evaluates the trend of whether the judgment results for multiple applicants U include many applicants U with high attractiveness levels. For example, as shown in FIG. 4, the evaluation unit 108 evaluates the trend of the feature amounts of all applicants U included in the judgment results.

[0037] The adjustment unit 109 is realized, for example, by the operation of a CPU. The adjustment unit 109 adjusts the feature quantities to be emphasized based on the evaluation results. The adjustment unit 109 performs adjustments, for example, to increase or decrease the weighting of specific feature quantities. The adjustment unit 109 externally acquires, for example, feature quantities to be adjusted and adjustment amounts for feature quantities included in the screen diagram shown in FIG. 4. The adjustment unit 109 adjusts the weighting based on the acquired feature quantities and adjustment amounts. For example, when it is desired to increase the pass recommendation rate for applicant U who has strengths in attractiveness, the adjustment unit 109 performs adjustments to increase the weighting for attractiveness.

[0038] The determination unit 110 is realized, for example, by the operation of a CPU. The determination unit 110 determines whether an applicant U who has been determined to have a predetermined or higher degree of recommendation for passing has exceeded the pass line. For example, the determination unit 110 determines that an applicant U who meets a predetermined pass line has exceeded the pass line. For example, the determination unit 110 determines whether an applicant U has exceeded the pass line based on a pass line that is predetermined according to the number of people to be passed.

[0039] 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 each applicant U. For example, as shown in FIG. 5, the fulfillment degree calculation unit 111 calculates the fulfillment degree of the attached label for each applicant U.

[0040] The analysis unit 112 is realized, for example, by the operation of a CPU. The analysis unit 112 analyzes the trends of the applicant U who has been determined to have exceeded the pass line. For example, the analysis unit 112 analyzes the trends of the feature values ​​of the applicant U who has exceeded the pass line.

[0041] The output unit 113 is realized, for example, by the operation of a CPU. The output unit 113 outputs the pass recommendation level for applicant U based on the judgment result. The output unit 113 also outputs the evaluation result by the evaluation unit 108, the satisfaction level calculated by the satisfaction level 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.

[0042] 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).

[0043] Next, it is determined whether or not a pass line needs to be determined (step S4). If a pass line determination is required (step S4: YES), the process proceeds to step S5. On the other hand, if a pass line determination is not required (step S4: NO), the process proceeds to step S7.

[0044] In step S5, the determination unit 110 determines whether or not the applicant U has passed a predetermined pass mark. Next, the satisfaction level calculation unit 111 calculates the satisfaction level (step S6). Next, the process proceeds to step S7.

[0045] In step S7, it is determined whether or not adjustment of the feature amount is necessary. If adjustment of the feature amount is necessary (step S7: YES), the process proceeds to step S8. On the other hand, if adjustment of the feature amount is not necessary (step S7: NO), the process proceeds to step S10.

[0046] In step S8, the evaluation unit 108 evaluates the result of the pass recommendation level judgment. Next, the adjustment unit 109 adjusts the weighting of the feature amount based on the adjustment amount acquired based on the judgment result (step S9). Next, the process proceeds to step S10.

[0047] In step S10, the output unit 113 outputs the determination result. The output unit 113 also outputs the determination result and the evaluation result as necessary. This completes the processing of this flow.

[0048] 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.

[0049] The program can be stored and supplied to a 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 Memory)). The display program may also be supplied to a 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 medium can supply the program to a computer via a wired communication path such as an electric wire or optical fiber, or via a wireless communication path.

[0050] As described above, the selection device 1 and the program according to this embodiment have 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 pass recommendation level for applicant U based on the features of 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 pass recommendation level for applicant U based on the judgment result. The program also causes a computer to function as a selection device 1 that selects applicant U for a specified business based on video images containing applicant U as a subject, and causes the computer to function as a video image acquisition unit 101 that acquires video images, a recommendation level judgment unit 106 that judges the degree of recommendation for applicant U to pass based on the 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 applicant U to pass based on the judgment results. This allows the degree of recommendation for applicant U to pass to be output according to the operator's wishes, making it easier to decide on successful applicants and determining which applicant U best suits the operator's wishes.

[0051] (2) The selection device 1 further includes an extraction unit 103 that extracts the feature amounts of the applicant U contained in the video. This makes it possible to easily obtain the feature amounts used for judgment from the video.

[0052] (3) The extraction unit 103 labels the applicant U based on the extracted features, and the recommendation level determination unit 106 determines the pass recommendation level for the applicant U based on the label attached to the applicant U, the labels attached to 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 better matches the operator's preferences based on the other applicants U.

[0053] (4) The selection device 1 further includes an evaluation unit 108 that evaluates the tendency of the judgment results 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.

[0054] (5) The recommendation level determination unit 106 determines the recommendation level for passing the applicant U based on the labels attached to the videos of past successful applicants. This makes it possible to determine the applicant U who better matches the operator's preferences.

[0055] (6) The selection device 1 further includes a judgment unit 110 that judges whether an applicant U who has been judged to have a predetermined or higher pass recommendation level has exceeded the pass line. This allows for pass / fail judgment to be performed, making the selection process easier.

[0056] (7) The selection device 1 further includes a fulfillment level calculation unit 111 that calculates the fulfillment level for the label for the applicant U. This makes it possible to easily obtain the reason for the judgment.

[0057] (8) The selection device 1 further includes an analysis unit 112 that analyzes the trends of the determined successful applicants. This makes it easy to determine whether the applicants actually meet the expectations of the business operator.

[0058] Although preferred embodiments of the selection device and program of the present invention have been described above, the present disclosure is not limited to the above-described embodiments and can be modified as appropriate.

[0059] For example, in the above embodiment, the recommendation level determination unit 106 determines the pass recommendation level based on past applicant U, but this is not limited to this. The recommendation level determination unit 106 may determine the pass recommendation level for actual applicant U by using another applicant U as the ideal image of applicant U that the business operator considers to be ideal.

[0060] Furthermore, in the above embodiment, the selection device 1 is described as including the extraction unit 103, but is not limited to this. Instead of the extraction unit 103, a feature amount already 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.

[0061] In the above embodiment, the recommendation level determination unit 106 may further determine the pass recommendation level based on information related to the status of the applicant U and external evaluation factors. The recommendation level determination unit 106 may determine the pass recommendation level based on, for example, information related to the career history and test results of the applicant U. The recommendation level determination unit 106 may also use the results of a dark color test as the external evaluation factors.

[0062] Furthermore, in the above embodiment, the selection device 1 may include a simulation execution unit 114 that simulates a pass / fail decision to a predetermined business using the acquired video. 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.

[0063] In the above embodiment, the selection device 1 may acquire video images of the applicant U for each of a plurality of businesses. The selection device 1 may also determine the degree of recommendation for passing for each of a plurality of businesses. The selection device 1 may acquire the video images from the user terminal 20 and also acquire the name of the business to which the applicant U wishes to apply.

[0064] In the above embodiment, the labels are not limited to those assigned based on features extracted from the video. For example, the labels may be adjusted and determined by creating a correct label based on a predetermined standard and a plurality of videos, and then using the difference between the correct label and the standard when determining the correct label for other plurality of videos.

[0065] 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.

[0066] In the above embodiment, the extraction unit 103 may extract attributes (such as athletics or international student) of the applicant U from the content included in the video. The recommendation level determination unit 106 may determine the recommendation level for passing based on the attributes.

[0067] Furthermore, in the above embodiment, the simulation execution unit 114 may make a judgment by combining information other than the video (meta information (information on proficiency tests, academic records, etc.)). Furthermore, 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. Furthermore, the pass / fail judgment by the simulation execution unit 114 may be indicated by a continuous value such as a pass rate.

[0068] In the above embodiment, the analysis unit 112 may also analyze the tendency of the applicant U who has been determined not to have passed the pass mark.

[0069] Furthermore, in the above embodiment, the selection device 1 is not limited to being used at the document screening stage before the first interview, but may be used at any stage in the selection process. [Explanation of symbols]

[0070] 1. Selection device 101 Video image acquisition unit 102 Video storage unit 103 Extraction part 106 Recommendation Judgment Section 108 Evaluation Department 109 Adjustment section 110 Judgment section 111 Satisfaction calculation unit 112 Analysis Department 113 Output section 114 Simulation execution unit U applicant

Claims

1. A selection device that selects applicants for a predetermined business based on a moving image including the applicants as subjects, a moving image acquisition unit that acquires the moving image; a recommendation level determination unit that determines the pass recommendation level of the applicant in accordance with criteria based on multiple 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 evaluation unit that evaluates the tendency of specific features common to a group of applicants with a high pass recommendation level based on the pass recommendation level judgment results for a plurality of applicants; an adjustment unit that adjusts a feature quantity to be emphasized among the plurality of feature quantities in the judgment criterion based on an evaluation result, and updates the judgment criterion based on the adjustment result; A selection device comprising:

2. The selection device according to claim 1 , further comprising an extraction unit that extracts the plurality of feature amounts of the applicant contained in the moving image.

3. 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.

4. the extraction unit performs labeling on the applicant based on the extracted plurality of feature amounts; 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 decisions of the other applicants.

5. The selection device according to claim 4 , wherein the recommendation level determination unit determines the applicant's recommendation level for passing based on the determination criteria that are further adjusted based on labels attached to video images of past successful applicants.

6. The selection device described in claim 5, wherein the recommendation level determination unit further 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 statements.

7. 7. The selection device according to claim 3, wherein the recommendation level determination unit determines the applicant's recommendation level for passing based on information relating to the applicant's status and external evaluation factors.

8. 8. The selection device according to claim 3, further comprising a determination unit that determines whether or not an applicant who has been determined to have a degree of recommendation for passing that is equal to or higher than a predetermined level has exceeded a passing grade.

9. The selection device according to claim 4 , further comprising a fulfillment level calculation unit that calculates the fulfillment level of the applicant with respect to the label.

10. The selection device according to claim 2 , further comprising a simulation execution unit that simulates a pass / fail decision for a predetermined business using the acquired moving image and outputs a simulation result.

11. 9. 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.

12. 9. The selection device according to claim 8, further comprising an analysis unit that analyzes the tendency of the applicant who has been determined not to have passed the pass line.

13. The selection device according to claim 2 , wherein the extraction unit sorts the acquired plurality of videos using a predetermined feature value among the plurality of feature values.

14. A program that causes a computer to function as a selection device that selects applicants for a predetermined business based on a video image that includes the applicants as subjects, The computer a moving image acquisition unit that acquires the moving image; a recommendation level determination unit that determines the pass recommendation level of the applicant in accordance with criteria based on multiple 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 evaluation unit that evaluates the tendency of specific features common to a group of applicants with a high pass recommendation level based on the pass recommendation level judgment results for a plurality of applicants; an adjustment unit that adjusts a feature quantity to be emphasized among the plurality of feature quantities in the judgment criterion based on an evaluation result, and updates the judgment criterion based on the adjustment result; A program that functions as a

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