System that automatically identifies a Candidate for hiring by using a composite score comprised of a Spec Score generated by a Candidates answers to questions and an Industry Score based on a database of key words & key texts compiled from source documents, such as job descriptions

• US 20090037235A1
• Filed: 07/30/2007
• Published: 02/05/2009
• Est. Priority Date: 07/30/2007
• Status: Abandoned Application

• Current Assignee: Anthony Au
• Original Assignee: Anthony Au
• Inventors: Au, Anthony
• Application Number:
  US11/830,839
• Publication Number:
  US 20090037235A1
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/1053 Employment or hiring

Abstract

A system that automatically identifies a Candidate for hiring by using a composite score comprised of a Spec Score generated by a Candidates answers to questions and an Industry Score based on key words & key texts from a source document, such as a job description. This system may reduce the time required to select a meaningful shortlist, as well as improving the compatibility of qualifications of candidates towards the requirements of a position by using a composite score. In doing so, the savings may result in reduction of both tangible and intangible costs currently incurred by an employer-company today.

Claims

1. A data processing system for scoring candidates comprising:

a) having a spec score,

b) having an industry score; and

c) using the industry score and the spec score to create a composite score.

2. A system according to claim 1 where said the spec score is based on a set of responsibilities, personal attributes and winning attributes.

3. A system according to claim 1 where said industry score is generated by

a) building a Key Word Library from a plurality of source documents,

b) building a Key Text Library from a plurality of source documents;

and

c) comparing a plurality of incoming document against the Key Text Library and the Key Word Library.

4. A system according to claim 3 in where said Key Word Library is build by identifying key words from a document.

5. A system according to claim 4 where said document is a resume.

6. A system according to claim 3 where said reference score is based on a combination of the industry and job function.

7. A system according to claim 3 which includes the step of compiling a list of pronouns and special terms.

8. A system according to claim 3 where each document will be parsed into key words.

9. A system according to claim 3 which includes eliminating all noise words until the amount of key words reaches a saturation state.

10. A system according to claim 1 further comprising using artificial intelligent to create said spec score.

11. A system according to claim 1 further comprising using artificial intelligent to create said industry score.

12. A system according to claim 1 further comprising having a spec score multiplier for said spec score and having an industry score multiplier for said industry score.

13. A system according to claim 12 where said spec score multiplier and said industry score multiplier add up to equal 1.

14. A system according to claim 12 where said composite score is calculated by adding the spec score multiplier times the spec score and adding the industry score multiplier times the industry reference score.

15. A data processing system for scoring candidates comprising:

having a spec score, having an industry score;

using the industry score and the spec score to create a composite score, where said the spec score is based on a set of responsibilities, personal attributes and winning attributes, where said industry score is generated by a) building a Key Word Library from a plurality of source documents, b) building a Key Text Library from a plurality of source documents; and

c) comparing a plurality of incoming document against the Key Text Library and the Key Word Library, having a spec score multiplier for said spec score and having an industry score multiplier for said industry score where said spec score multiplier and said industry score multiplier add up to equal 1 and the composite score is calculated by adding the spec score multiplier times the spec score and adding the industry score multiplier times the industry score.

16. A system according to claim 15 in where said Key Word Library is build by identifying key words from a document.

17. A system according to claim 16 where said document is a resume.

18. A system according to claim 15 where said industry score is based on a combination of the industry and job function.

19. A system according to claim 15 further comprising using artificial intelligent to create said spec score.

20. A system according to claim 15 further comprising using artificial intelligent to create said industry score.