Development of electronic employee selection systems and methods

• US 20050114279A1
• Filed: 08/11/2004
• Published: 05/26/2005
• Est. Priority Date: 08/03/2000
• Status: Active Grant

• Current Assignee: Cadient LLC
• Original Assignee: Unicru, Inc. (Seahawk Acquisition Corp.)
• Inventors: Smith, David R., Smith, John R., Becker, Richard W., Clainos, Deme M., Scarborough, David J., Chambless, Bjorn, Check, Thomas F., Eng, Maxwell W., Mertz, Adam N., Paajanen, George E., Levy, Joel R.
• Granted Patent: US 7,562,059 B2
• Time in Patent Office: Days
• US Class Current: 706/21
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/063118 Staff planning in a project environment
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 10/1053 Employment or hiring
• CPC Class Codes: G06Q 30/08 Auctions
• CPC Class Codes: G09B 5/00 Electrically-operated educational appliances working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F
• CPC Class Codes: G09B 7/00 Electrically-operated teaching apparatus or devices working with questions and answers mechanically operated G09B3/00; computing arrangements G06F
• CPC Class Codes: G09B 7/02 of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student

Abstract

An automated employee selection system can use a variety of techniques to provide information for assisting in selection of employees. For example, pre-hire and post-hire information can be collected electronically and used to build an artificial-intelligence based model. The model can then be used to predict a desired job performance criterion (e.g., tenure, number of accidents, sales level, or the like) for new applicants. A wide variety of features can be supported, such as electronic reporting. Pre-hire information identified as ineffective can be removed from a collected pre-hire information. For example, ineffective questions can be identified and removed from a job application. New items can be added and their effectiveness tested. As a result, a system can exhibit adaptive learning and maintain or increase effectiveness even under changing conditions.

Claims

1. A method of constructing a model predicting employment performance based on a set of input employment parameters, the method comprising:

selecting a set of input parameters indicating pre-hire characteristics of an employee, wherein the pre-hire characteristics are available before hiring the employee and are collected electronically from the employee;

selecting a set of output parameters indicating post-hire outcomes available after hiring the employee; and

training a neural network with the input and output parameters.

2. The method of claim 1 further comprising:

choosing a set of one or more candidate characteristics, wherein the characteristics indicate data available before hiring an employee;

testing effectiveness of the candidate characteristics in predicting the post-hire characteristics; and

responsive to determining the candidate information is effective, incorporating the candidate information into the model.

11. One or more computer-readable media having computer-executable instructions for performing the method of claim 1.

3. A method for constructing an artificial intelligence-based employment selection process based on pre-hire information comprising personal employee characteristics and post-hire information comprising employee job performance observation information, the method comprising:

generating a plurality of predictive artificial intelligence models based on the pre-hire and post-hire information, wherein at least two of the artificial intelligence models are of different types;

testing effectiveness of the models to select an effective model; and

applying the effective model to predict post-hire information not yet observed;

wherein at least one of the models is a neural network.

4. The method of claim 3 wherein at least one of the models is an expert system.

12. One or more computer-readable media having computer-executable instructions for performing the method of claim 3.

5. A method for constructing an artificial intelligence-based employment selection process based on pre-hire information comprising personal employee characteristics and post-hire information comprising employee job performance observation information, the method comprising:

generating a plurality of predictive artificial intelligence models based on the pre-hire and post-hire information, wherein at least two of the artificial intelligence models are of different types;

testing effectiveness of the models to select an effective model; and

applying the effective model to predict post-hire information not yet observed;

wherein at least one of the models is a fuzzy logic system.

13. One or more computer-readable media having computer-executable instructions for performing the method of claim 5.

6. A method for constructing an artificial intelligence-based employment selection process based on pre-hire information comprising personal employee characteristics and post-hire information comprising employee job performance observation information, the method comprising:

generating a plurality of predictive artificial intelligence models based on the pre-hire and post-hire information, wherein at least two of the artificial intelligence models are of different types;

testing effectiveness of the models to select an effective model; and

applying the effective model to predict post-hire information not yet observed;

wherein at least one of the models is an information theoretic model.

7. A method for constructing an artificial intelligence-based employment selection process based on pre-hire information comprising personal employee characteristics and post-hire information comprising employee job performance observation information, the method comprising:

generating a plurality of predictive artificial intelligence models based on the pre-hire and post-hire information, wherein at least two of the artificial intelligence models are of different types;

testing effectiveness of the models to select an effective model; and

applying the effective model to predict post-hire information not yet observed;

wherein at least one of the models is a neuro-fuzzy model.

8. A method of constructing a model generating one or more job performance criteria predictors based on input pre-hire information, the method comprising:

from a plurality of applicants, electronically collecting pre-hire information from the applicants;

collecting post-hire information for the applicants based on job performance of the applicants after hire;

from the pre-hire information and the post-hire information, generating an artificial intelligence-based predictive model operable to generate one or more job performance criteria predictors based on input pre-hire information from new applicants;

limiting the applicants for the model to those from a particular geographic area; and

constructing the model as a geographically-specialized model.

9. A method of constructing a model generating one or more job performance criteria predictors based on input pre-hire information, the method comprising:

from a plurality of applicants, electronically collecting pre-hire information from the applicants;

collecting post-hire information for the applicants based on job performance of the applicants after hire;

from the pre-hire information and the post-hire information, generating an artificial intelligence-based predictive model operable to generate one or more job performance criteria predictors based on input pre-hire information from new applicants;

limiting the applicants for the model to those with a particular educational level; and

constructing the model as an educational level-specialized model.

10. A computer-implemented method of refining an artificial-intelligence based employee performance selection system, the method comprising:

collecting information via an electronic device presenting a set of questions to employment candidates, wherein the questions are stored in a computer-readable medium;

testing effectiveness of at least one of the questions in predicting the post-hire information; and

responsive to determining the question is ineffective, deleting the question from the computer-readable medium;

wherein effectiveness comprises predictiveness tested based on information theoretic techniques.

14. An apparatus for assisting in determining the suitability of an individual for employment by an employer, the apparatus comprising:

an electronic data interrogator means for presenting a first set of a plurality of means for questioning to the individual;

an electronic answer capturer means for electronically storing the individual'"'"'s responses to at least a selected plurality of the first set of means for questioning presented to the individual;

an electronic predictor means responsive to the stored answers and for predicting at least one post-hire outcome if the individual were to be employed by the employer, the predictor means providing a prediction of the outcome based upon correlations of the stored answers with answers to sets of means for questioning by other individuals for which post-hire information has been collected; and

an electronic results provider means for providing an output indicative of the outcome to assist in determining the suitability of the individual for employment by the employer;

wherein the electronic predictor means comprises at least one neural network.