Skill Extraction Engine for Objective Personnel Ranking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Identifying the most suitable individual for a specific task is challenging due to subjective assessment criteria and the difficulty in accurately evaluating skill strengths, especially in rapidly evolving fields like software development where resumes become outdated as technologies shift and complexity increases.
Innovation Solution
A method and system that extract skills from content sources, such as version control repositories, by associating keywords with individual interactions, generating scores based on interaction frequency and recency, and ranking individuals through a traversable graph to provide an objective assessment of skill strengths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If subjective assessment criteria are used to evaluate skill strength, then individuals can self-assess their abilities, but the accuracy and objectivity of skill evaluation deteriorates
Solution Approach 1:
The system enables self-service by automatically extracting skill information from content sources without requiring manual self-assessment. Individuals are associated with skills based on their interactions with work items, and the system autonomously generates skill scores, eliminating the need for subjective individual evaluation while maintaining objective measurement.
Solution Approach 2:
The patent replaces the mechanical system of manual self-assessment with an automated information processing system. Instead of individuals subjectively evaluating their own skills, the system uses algorithms to extract skill information from work items, content sources, and interaction data, substituting human judgment with objective computational analysis.
2Duration of action of stationary object
If resumes are used to document skills over long periods, then individuals can maintain a record of their knowledge, but the accuracy of current skill level deteriorates as technologies shift and complexity increases
Solution Approach 1:
The system performs preliminary action by continuously extracting and updating skill information from current work items and content sources before assessment is needed. Rather than relying on outdated resume data, the system proactively maintains up-to-date skill profiles by processing recent interactions with work items, ensuring the skill information reflects current capabilities rather than historical records.
Solution Approach 2:
The patent implements dynamics by making skill information continuously updateable and time-sensitive. The system processes work items and content sources in real-time, allowing skill profiles to dynamically adapt to new technologies and changing expertise. This dynamic approach replaces static resume data with living, continuously updated skill assessments that reflect current competency levels.
3Measurement precision
If detailed skill extraction from content sources is implemented, then specific and accurate skills can be identified, but the complexity of the assessment system increases
Solution Approach 1:
The system achieves universality by using a multi-functional platform that handles multiple tasks: extracting skills from various content sources, associating individuals with skills, generating scores, and maintaining databases. This unified system processes different types of work items and content sources through a common framework, reducing overall system complexity compared to separate specialized systems for each function.
Solution Approach 2:
The patent uses an intermediary approach by introducing a structured database and standardized association mechanisms between individuals, skills, and work items. This intermediary layer organizes the complex relationships between various content sources and skill assessments, making the system manageable through standardized data structures and processing protocols rather than direct complex interactions.
4Measurement precision
If skill scores are normalized over a pool of individuals, then comparative ranking can be achieved, but the ability to assess absolute skill strength deteriorates
Solution Approach 1:
The system implements nested doll by creating hierarchical skill scoring where individual skill scores are nested within normalized comparative rankings. The absolute skill strength is preserved at the individual score level, while the normalized ranking provides comparative context. This nested structure allows both absolute and relative measurements to coexist, with the individual skill assessment nested within the broader pool comparison.
Data Source
AI summary
Methods and systems are provided that are directed to identifying an individual having a specific skill and that is best suited for performing a particular task. The individual may be identified based on having a highest score for the skill out of a pool of individuals having the same skill. In examples, a score specific to each individual out of the pool of individuals having the same skill may be based on multiple interactions with the skill and may be adjusted based on a recency of when each interaction occurred and a level of involvement the individual had with the skill. Accordingly, individuals having recent experience with the skill may be scored, and therefore, ranked higher than individuals having experience with the skill that may have occurred further back in time. In examples, the information about the skills and experiences may be utilized to automatically generate the score.