Role Metadata Augmentation via ML Competency Inference
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Solution Overview
Problem
Traditional role descriptions lack detailed competencies, making it difficult for managers and interviewers to assess job-fit and provide effective feedback, as they do not explicitly include functional and foundational skills required for a role.
Innovation Solution
A computer-implemented process using a machine learning engine to preprocess role descriptions, infer competencies, and aggregate them based on similarity scores, adjusting proficiency levels according to band level and competency type, and selecting a predetermined number of foundational and functional competencies to augment role metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional role descriptions are used, then the role description is simple and easy to understand, but it lacks detailed competencies making it difficult to assess job-fit and provide effective feedback
Solution Approach 1:
The patent segments the role description into distinct components: traditional role description fields (summary, responsibilities, qualifications) and a new metadata section containing competencies with proficiency levels. This segmentation allows the role description to maintain its traditional simple structure while adding detailed competency information in a separate, organized manner, thus resolving the contradiction between information completeness and structural simplicity.
Solution Approach 2:
The patent embeds competency metadata within the role description structure, nesting the detailed competency information (including functional and foundational competencies with proficiency levels) inside the existing role description framework. This nesting approach allows the enhanced role description to contain comprehensive competency data without creating a completely new complex structure, thereby reducing information loss while managing structural complexity.
2Loss of information
If competencies are manually added to role descriptions, then detailed competency information is provided, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent implements a machine learning system that automatically extracts competencies from role description text and generates the competency metadata without requiring manual intervention. The system processes the role description text, identifies relevant competencies, assigns proficiency levels, and structures the output automatically, thereby providing detailed competency information while eliminating the time-consuming manual addition process.
Solution Approach 2:
The patent replaces the mechanical manual process of adding competencies to role descriptions with an automated machine learning system. The ML system uses natural language processing and pattern recognition to automatically extract and structure competency information from role description text, substituting the manual mechanical process with an automated intelligent system that achieves the same outcome faster and more efficiently.
3Loss of information
If multiple competency sources are aggregated, then comprehensive competency coverage is achieved, but the complexity of processing and prioritizing competencies increases
Solution Approach 1:
The patent incorporates a prioritization mechanism that uses feedback from multiple competency sources to determine the most relevant competencies for each role. The system processes competencies from various sources (role description text, talent framework, organizational data) and uses prioritization logic to filter and rank them, thereby achieving comprehensive competency coverage while managing processing complexity through intelligent selection rather than treating all inputs equally.
Solution Approach 2:
The patent applies local quality by differentiating between types of competencies (functional vs. foundational) and assigning different weights or priorities to them based on their specific characteristics and relevance to the role. This allows the system to handle the complexity of multiple competency sources by treating different competency types differently, achieving comprehensive coverage while reducing overall processing complexity through targeted differentiation.
Data Source
AI summary
A computer hardware system includes a machine learning engine and a hardware processor configured to perform the following executable operations. Text of a role description of a role having a role title is preprocessed. Competencies are inferred from the role description; using the machine learning engine. Competencies are identified from titles in a talent framework being similar to the title using the machine learning engine. The competencies are aggregated into an aggregation of competencies. The competencies in the aggregation are ordered based upon aggregated similarity scores. A proficiency level associated with each of the competencies in the aggregation is adjusted based upon band level and competency type. A plurality of competencies are selected. The role is augmented with metadata that includes the selected plurality of competencies and proficiency levels associated therewith.