SYSTEMS AND METHODS FOR EVALUATING JOB CANDIDATES

1 . A system for evaluating a job candidate, comprising:

a node comprising a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, where the plurality of instructions comprises:

instructions, that when executed, present a plurality of interview questions to a job candidate;

instructions, that when executed, receive the job candidate's verbal answers to the questions;

instructions, that when executed, record the answers;

instructions, that when executed, apply a linguistic algorithm to text of the answers, wherein the linguistic algorithm is trained to find identified keywords and comprises a psychological behavioral model; and

instructions, that when executed, output a score for the job candidate based on a density of the identified keywords in the answers.

2 . The system of claim 1 , wherein the plurality of instructions do not include instructions to record the interview questions.

3 . The system of claim 1 , wherein the score is associated with one or more of engagement, motivation, distress, or personality type.

4 . The system of claim 3 , wherein the score for engagement, motivation, and distress is independently selected as high, medium, or low.

5 . The system of claim 3 , wherein the score for the personality type is an assessment that the job candidate is one of a thoughts, opinions, reactions, or emotions type.

6 . The system of claim 3 , further comprising instructions, that when executed, aggregate the scores for engagement, motivation, distress, and personality type into an overall interview score.

7 . The system of claim 1 , further comprising instructions, that when executed, analyze video of the job candidate including the verbal answers.

8 . The system of claim 1 , further comprising instructions, that when executed, correlate the score to direct feedback on the job candidate after being hired and/or on the job performance.

9 . A method of evaluating a job candidate, which comprises:

identifying, by one or more processors, keywords associated with one or more categories relevant to a job position;

presenting, by the one or more processors, a plurality of interview questions to the job candidate;

receiving, by the one or more processors, the job candidate's answers to the interview questions;

analyzing, by the one or more processors, the answers in a text form for the identified keywords relevant to a psychological behavioral model; and

outputting, by the one or more processors, a score for the one or more categories based on a density of the identified keywords in the analyzed answers.

10 . The method of claim 9 , wherein the one or more categories comprise engagement, motivation, distress, or personality type.

11 . The method of claim 10 , wherein the score for engagement, motivation, and distress is independently selected to be high, medium, or low.

12 . The method of claim 10 , wherein the score for the personality type comprises assessing the candidate's personality as one of a thoughts, opinions, reactions, or emotions type.

13 . The method of claim 9 , further comprising aggregating the scores for engagement, motivation, distress, and personality type into an overall interview score.

14 . The method of claim 9 , further comprising analyzing video of the job candidate including the verbal answers.

15 . The method of claim 9 , further comprising correlating the scores to direct feedback on the job candidate after being hired and/or on the job performance.

16 . The method of claim 9 , wherein the job candidate is remote from an interviewer.

17 . A non-transitory computer readable article comprising a plurality of instructions comprising:

instructions, that when executed, present a plurality of interview questions to a job candidate;

instructions, that when executed, receive the job candidate's verbal answers to a the questions;

instructions, that when executed, score the job candidate's answers for a plurality of categories using at least one linguistic algorithm that scores the answers based on the density of keywords and that comprises a psychological behavioral model;

instructions, that when executed, aggregate the score for each category into an overall interview score; and

instructions, that when executed, display the overall interview score.

18 . The non-transitory computer readable article of claim 17 , wherein each category comprises one or more of engagement, motivation, distress, or personality type.

19 . The non-transitory computer readable article of claim 18 , wherein the score for engagement, motivation, and distress is each independently high, medium, or low.

20 . The non-transitory computer readable article of claim 18 , wherein the score for the personality type is an assessment that the candidate is one of a thoughts, opinions, reactions, or emotions type.

21 . The non-transitory computer readable article of claim 17 , wherein the linguistic algorithm identifies keywords associated with each category.

22 . The non-transitory computer readable article of claim 17 , further comprising instructions, that when executed, analyze video of the job candidate including at least the verbal answers.

23 . An apparatus for evaluating job candidates, which comprises:

a storage device storing a computer readable program; and

a processor executing the computer readable program, the computer readable program comprising:

a display module configured to present a plurality of interview questions to job candidates and output a score for each job candidate.

a recording module configured to receive and record a plurality of verbal answers from the job candidates; and

a scoring module configured to:

receive identified keywords associated with predefined criteria for a position,

apply an algorithm based on a psychological behavioral model trained to find the number of times the keywords are used in the answers, and

score the answers for each job candidate using the trained algorithm.