System and method for productizing human capital labor employment positions/jobs

• US 8,788,357 B2
• Filed: 08/12/2010
• Issued: 07/22/2014
• Est. Priority Date: 08/12/2009
• Status: Active Grant

• Current Assignee: IQNavigator Incorporated
• Original Assignee: IQNavigator Incorporated
• Inventors: Wasicek, Wyatt, Thornburg, Scott C., Cullen, Andrew A. III
• Primary Examiner(s): Civan, Ethan D
• Application Number:
  US12/855,532
• Publication Number:
  US 20110040695A1
• Time in Patent Office: 1,440 Days
• Field of Search: 705 26- 272
• US Class Current: 705/26.1
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/105 Human resources

Abstract

A method includes configuring a human-capital-management (HCM) master taxonomy and a HCM language library. The HCM master taxonomy includes a plurality of levels that range from more general to more specific, each level of the plurality of levels comprising a plurality of nodes. The plurality of levels include a job-species level and a job-family level, the job-species level including a level of greatest specificity in the plurality of levels, the job-family level including a level of specificity immediately above the job-species level. In addition, the method includes transforming human-capital information via the HCM language library. Further, the method includes classifying the transformed human-capital information into a job-family node selected from the plurality of nodes at the job-family level.

Claims

1. A method comprising:

configuring, by a computer system comprising computer hardware, a human-capital-management (HCM) master taxonomy and a HCM language library;

wherein the HCM master taxonomy comprises a plurality of levels that range from more general to more specific, each level of the plurality of levels comprising a plurality of nodes;

wherein the plurality of levels comprises a job-species level and a job-family level, the job-species level comprising a level of greatest specificity in the plurality of levels, the job-family level comprising a level of specificity immediately above the job-species level;

transforming, by the computer system, human-capital information via the HCM language library;

classifying, by the computer system the transformed human-capital information into a job-family node selected from the plurality of nodes at the job-family level;

analyzing, by the computer system, selected attributes of a plurality of job-species nodes, the plurality of job species comprising ones of the plurality of nodes at the job-species level that are positioned beneath the job-family node; and

wherein the analyzing comprises;

identifying differences between node attributes of the plurality of job species;

for each identified difference of the identified differences, analyzing an impact of the identified difference on a spotlight attribute; and

determining one or more of the node attributes to be key performance indicators (KPIs) for the spotlight attribute.

2. The method of claim 1, wherein configuring a HCM library and a HCM master taxonomy comprises creating a plurality of subject dictionaries, the HCM library comprising the plurality of subject dictionaries.

3. The method of claim 2, wherein configuring a HCM library and a HCM master taxonomy comprises integrating standard dictionary words and terms into the plurality of subject dictionaries.

4. The method of claim 2, wherein configuring a HCM library and a HCM master taxonomy comprises creating and populating at least one HCM-contextual dictionary selected from a group consisting of:

an abbreviation dictionary, an inference dictionary and a noise-words dictionary.

5. The method of claim 2, wherein the plurality of subject dictionaries comprises a job dictionary, an organization dictionary, a product dictionary, a date dictionary, a place dictionary and a person dictionary.

6. The method of claim 1, wherein the transforming comprises:

parsing the human-capital information to yield a plurality of linguistic units; and

mapping the plurality of linguistic units to a plurality of subject dictionaries, the plurality of subject dictionaries defining a HCM vector space.

7. The method of claim 6, wherein the mapping comprises projecting the plurality of linguistic units onto the HCM vector space, the projecting yielding a multidimensional vector.

8. The method of claim 7, wherein the mapping comprises:

for each linguistic unit of the plurality of linguistic units, producing one or more possible meanings for the linguistic unit; and

wherein each of the one or more possible meanings has magnitude and direction relative to the HCM vector space.

9. The method of claim 8, wherein the production of the one or more possible meanings comprises, for each linguistic unit of the plurality of linguistic units:

performing a spell check; and

referencing an inference dictionary, the HCM language library comprising the inference dictionary.

10. The method of claim 6, wherein:

the human-capital information comprises unstructured data; and

the parsing comprises linguistically analyzing the plurality of linguistic units.

11. The method of claim 6, wherein:

the human-capital information comprises structured data; and

the parsing comprises following a known structure for the structured data to obtain the plurality of linguistic units.

12. The method of claim 7, wherein the classifying comprises:

measuring a distance between the vector-space projection and a vector-space measurement at each node of the plurality of nodes for at least a portion of the plurality of levels;

determining a placement of the transformed human-capital information into the family node based on the measured distance.

13. The method of claim 12, wherein the measuring comprises measuring a distance between the vector-space projection and a vector-space measurement at each node of the plurality of nodes for each level of the plurality of levels that is above the job-species level.

14. The method of claim 12, the method comprising:

wherein, for each of the plurality of levels, each node of the plurality of nodes comprises a plurality of node attributes, each node attribute of the plurality of node attributes having associated therewith a bit flag;

wherein the bit flag comprises performance-optimization information regarding one or more siblings of the node; and

via the performance-optimization information, determining that the one or more siblings need not be measured in the measuring responsive to a condition for action being satisfied.

15. The method of claim 1, wherein the analyzing of an impact upon the spotlight attribute comprises statistically measuring the impact.

16. The method of claim 15, wherein the spotlight attribute is a pay rate for a human resource.

17. The method of claim 1, the method comprising determining, via the determined KPIs, that the transformed human-capital information should be placed into one of the plurality of job-species nodes.

18. The method of claim 1, the method comprising determining, via the determined KPIs, that a new job-species node should be created.

19. The method of claim 17, the method comprising:

responsive to the determination that the transformed human-capital information should be placed into one of the plurality of job-species node comprises, classifying the transformed human-capital information into a selected job-species node from the plurality of job-species nodes.

20. The method of claim 18, the method comprising:

responsive to the determination that a new job-species node should be created, configuring a new job-species node beneath the job-family node.

21. The method of claim 1, wherein:

the plurality of levels of the HCM master taxonomy comprises a job-domain level, a job-category level, a job-subcategory level and a job-class level;

the job-class level comprises a level of specificity immediately above the job-family level;

the job-subcategory level comprises a level of specificity immediately above the job-class level;

the job-category level comprises a level of specificity immediately above the job-subcategory level; and

the job-domain level comprises a level of specificity immediately above the job-category level.

22. A computer-program product comprising a non-transitory computer-usable medium having computer-readable program code embodied therein, the computer-readable program code adapted to be executed to implement a method comprising:

configuring a human-capital-management (HCM) master taxonomy and a HCM language library;

wherein the HCM master taxonomy comprises a plurality of levels that range from more general to more specific, each level of the plurality of levels comprising a plurality of nodes;

wherein the plurality of levels comprises a job-species level and a job-family level, the job-species level comprising a level of greatest specificity in the plurality of levels, the job-family level comprising a level of specificity immediately above the job-species level;

transforming human-capital information via the HCM language library;

classifying the transformed human-capital information into a job-family node selected from the plurality of nodes at the job-family level;

analyzing selected attributes of a plurality of job-species nodes, the plurality of job species comprising ones of the plurality of nodes at the job-species level that are positioned beneath the job-family node; and

wherein the analyzing comprises;

identifying differences between node attributes of the plurality of job species;

for each identified difference of the identified differences, analyzing an impact of the identified difference on a spotlight attribute; and

determining one or more of the node attributes to be key performance indicators (KPIs) for the spotlight attribute.

23. The computer-program product of claim 22, wherein configuring a HCM library and a HCM master taxonomy comprises creating a plurality of subject dictionaries, the HCM library comprising the plurality of subject dictionaries.

24. The computer-program product of claim 23, wherein configuring a HCM library and a HCM master taxonomy comprises integrating standard dictionary words and terms into the plurality of subject dictionaries.

25. The computer-program product of claim 23, wherein configuring a HCM library and a HCM master taxonomy comprises creating and populating at least one HCM-contextual dictionary selected from a group consisting of:

an abbreviation dictionary, an inference dictionary and a noise-words dictionary.

26. The computer-program product of claim 23, wherein the plurality of subject dictionaries comprises a job dictionary, an organization dictionary, a product dictionary, a date dictionary, a place dictionary and a person dictionary.

27. The computer-program product of claim 22, wherein the transforming comprises:

parsing the human-capital information to yield a plurality of linguistic units; and

mapping the plurality of linguistic units to a plurality of subject dictionaries, the plurality of subject dictionaries defining a HCM vector space.

28. The computer-program product of claim 27, wherein the mapping comprises projecting the plurality of linguistic units onto the HCM vector space, the projecting yielding a multidimensional vector.

29. The computer-program product of claim 28, wherein the mapping comprises:

for each linguistic unit of the plurality of linguistic units, producing one or more possible meanings for the linguistic unit; and

wherein each of the one or more possible meanings has magnitude and direction relative to the HCM vector space.

30. The computer-program product of claim 29, wherein the production of the one or more possible meanings comprises, for each linguistic unit of the plurality of linguistic units:

performing a spell check; and

referencing an inference dictionary, the HCM language library comprising the inference dictionary.

31. The computer-program product of claim 27, wherein:

the human-capital information comprises unstructured data; and

the parsing comprises linguistically analyzing the plurality of linguistic units.

32. The computer-program product of claim 27, wherein:

the human-capital information comprises structured data; and

the parsing comprises following a known structure for the structured data to obtain the plurality of linguistic units.

33. The computer-program product of claim 28, wherein the classifying comprises:

measuring a distance between the vector-space projection and a vector-space measurement at each node of the plurality of nodes for at least a portion of the plurality of levels;

determining a placement of the transformed human-capital information into the family node based on the measured distance.

34. The computer-program product of claim 33, wherein the measuring comprises measuring a distance between the vector-space projection and a vector-space measurement at each node of the plurality of nodes for each level of the plurality of levels that is above the job-species level.

35. The computer-program product of claim 33, the method comprising:

wherein, for each of the plurality of levels, each node of the plurality of nodes comprises a plurality of node attributes, each node attribute of the plurality of node attributes having associated therewith a bit flag;

wherein the bit flag comprises performance-optimization information regarding one or more siblings of the node; and

via the performance-optimization information, determining that the one or more siblings need not be measured in the measuring responsive to a condition for action being satisfied.

36. The computer-program product of claim 22, wherein the analyzing of an impact upon the spotlight attribute comprises statistically measuring the impact.

37. The computer-program product of claim 36, wherein the spotlight attribute is a pay rate for a human resource.

38. The computer-program product of claim 22, the method comprising determining, via the determined KPIs, that the transformed human-capital information should be placed into one of the plurality of job-species nodes.

39. The computer-program product of claim 22, the method comprising determining, via the determined KPIs, that a new job-species node should be created.

40. The computer-program product of claim 38, the method comprising:

responsive to the determination that the transformed human-capital information should be placed into one of the plurality of job-species node comprises, classifying the transformed human-capital information into a selected job-species node from the plurality of job-species nodes.

41. The computer-program product of claim 38, the method comprising:

responsive to the determination that a new job-species node should be created, configuring a new job-species node beneath the job-family node.

42. The computer-program product of claim 22, wherein:

the plurality of levels of the HCM master taxonomy comprises a job-domain level, a job-category level, a job-subcategory level and a job-class level;

the job-class level comprises a level of specificity immediately above the job-family level;

the job-subcategory level comprises a level of specificity immediately above the job-class level;

the job-category level comprises a level of specificity immediately above the job-subcategory level; and

the job-domain level comprises a level of specificity immediately above the job-category level.