# US 8,788,357 B2 · System and method for productizing human capital labor employment positions/jobs

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- Number: US 8,788,357 B2
- Document ID: US8788357B2
- Status: Granted
- Kind: B2
- Application number: 12/855,532
- Filed: 2010-08-12
- Published: 2011-02-17
- Granted: 2014-07-22
- Earliest claimed: 2009-08-12
- Assignees: IQNavigator, Inc.
- Applicants: Wyatt Wasicek; Scott C. Thornburg; Andrew A. Cullen, III
- Inventors: Wyatt Wasicek; Scott C. Thornburg; Andrew A. Cullen, III
- Web page: https://patentgrep.com/patent/US8788357B2

## CPC

- G06Q 10/06: Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q 10/105: Human resources
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## 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

### Claim 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.

### Claim 2 (depends on 1)

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.

### Claim 3 (depends on 2)

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.

### Claim 4 (depends on 2)

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.

### Claim 5 (depends on 2)

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.

### Claim 6 (depends on 1)

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.

### Claim 7 (depends on 6)

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.

### Claim 8 (depends on 7)

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.

### Claim 9 (depends on 8)

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.

### Claim 10 (depends on 6)

The method of claim 6, wherein:
the human-capital information comprises unstructured data; and
the parsing comprises linguistically analyzing the plurality of linguistic units.

### Claim 11 (depends on 6)

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.

### Claim 12 (depends on 7)

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.

### Claim 13 (depends on 12)

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.

### Claim 14 (depends on 12)

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.

### Claim 15 (depends on 1)

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

### Claim 16 (depends on 15)

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

### Claim 17 (depends on 1)

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.

### Claim 18 (depends on 1)

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

### Claim 19 (depends on 17)

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.

### Claim 20 (depends on 18)

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.

### Claim 21 (depends on 1)

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.

### Claim 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.

### Claim 23 (depends on 22)

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.

### Claim 24 (depends on 23)

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.

### Claim 25 (depends on 23)

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.

### Claim 26 (depends on 23)

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.

### Claim 27 (depends on 22)

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.

### Claim 28 (depends on 27)

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.

### Claim 29 (depends on 28)

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.

### Claim 30 (depends on 29)

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.

### Claim 31 (depends on 27)

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.

### Claim 32 (depends on 27)

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.

### Claim 33 (depends on 28)

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.

### Claim 34 (depends on 33)

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.

### Claim 35 (depends on 33)

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.

### Claim 36 (depends on 22)

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

### Claim 37 (depends on 36)

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

### Claim 38 (depends on 22)

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.

### Claim 39 (depends on 22)

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

### Claim 40 (depends on 38)

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.

### Claim 41 (depends on 38)

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.

### Claim 42 (depends on 22)

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.