Patent ID: 11861534
Assignee: nan
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 11:
12. A method to automate selection of qualified interviewers for an interview of a candidate for a job opening at an organization, the method comprising:
obtaining, by a processing device, first data points associated with the job opening from a first plurality of sources, and generating, based on the first data points, a calibrated job profile, wherein the calibrated job profile further comprises vector representations of variables derived from a profile of a qualified candidate previously hired for a same or similar position within the organization or predicted based on candidates previously hired for a same or similar position within the organization;
obtaining, by the processing device, second data points associated with the candidate from a second plurality of sources, and generating, based on the second data points, an enriched talent profile of the candidate;
identifying, by the processing device by comparing the calibrated job profile and the enriched talent profile, one or more aspects of the candidate to be evaluated during the interview;
receiving, by the processing device, enriched talent profiles of potential interviewers of the organization;
executing, by the processing device, a neural network module using, as inputs, the one or more aspects, the enriched talent profile of the candidate, and the enriched talent profiles of the potential interviewers to calculate, as outputs, plurality of ranked lists, the neural network module comprising an input layer to receive inputs and an output layer to output the plurality of ranked lists that each contains one or more potential interviewers that are ranked according to match scores indicating an effectiveness measure of a corresponding one of the one or more potential interviewers for evaluating a corresponding aspect of the candidate during the interview, wherein parameters of the neural network module are adjusted using an iterative training in one or more training sessions using training data including enriched talent profiles of example qualified interviewers, and wherein the iterative training of the neural network module iteratively includes: calculating using the neural network, in a forward propagation, predicted ranked lists that are ranked according to match scores indicating the effectiveness measure of a corresponding example interviewer for evaluating training aspect of example candidate, calculating a difference value between the predicted ranked lists and target ranked lists in a backward propagation, and adjusting one or more parameters of the neural network module based on the difference value between the predicted ranked lists and the target ranked lists;
determining, based on time slots in an interview agenda and calendars of the qualified interviewers, availabilities of interviewers during the interview, wherein the availabilities of the interviewers comprise part time availability for being available less than one time slot;
determining, by the processing device based on plurality of ranked lists and rules, qualified interviewers from the potential interviewers for evaluating each of the one or more aspects during the interview, wherein the rules comprise taking account of match scores for interviewers that are available and time weighted match scores for interviewers that are part time available, and wherein a time weighted match score is calculated based on a percentage of available time over a time slot that is weighted over a match score associated with an interviewer; and
transmitting a request for participating in an interview of the candidate to at least one of the qualified interviewers.