Kronos Talent Management Inc.

Unicru, Inc.

Bjorn Chambless

David J Scarborough

David Smith

Joel R Levy

George E Paajanen

Adam N Mertz

Thomas F Check

John R Smith

Deme M Clainos

Richard W Becker

Maxwell W Eng

Anne Thissen-Roe

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.

Electronic employee selection systems and methods

Development of electronic employee selection systems and methods

A plurality of neural networks or other models can be used in employee selection technologies. A hiring recommendation can be based at least on processing performed by a plurality of neural networks. For example, parallel or series processing by neural networks can be performed. A neural network can be coupled to one or more other neural networks. A binary or other n-ary output can be generated by one or more of the neural networks. In a series arrangement, candidates can be processed sequentially in multiple stages, and those surviving the stages are recommended for hire.

Employee selection via multiple neural networks

Growing community of inventors

Portland, OR, United States of America

Bjorn Chambless