Expertise Profiling System Using Machine Learning for EB-1A Assessment
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Solution Overview
Problem
Current technologies lack the use of artificial intelligence for generating individual profiles based on expertise and do not provide recommendations to enhance modeling expertise, making it difficult to assess and showcase extraordinary abilities for EB-1A green card applications.
Innovation Solution
A system and method utilizing machine learning models to analyze individual information and generate expertise profiles that characterize expertise based on learning states, progressive experience, and impact factors, including data, information, skill, knowledge, and wisdom stages, and providing recommendations to improve these aspects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to generate expertise profiles, then the precision of expertise assessment is improved, but the complexity of the system increases
Solution Approach 1:
The expertise profile is segmented into five distinct stages (data stage, information stage, skill stage, knowledge stage, wisdom stage), each with specific criteria and indicators. This segmentation allows the complex assessment to be broken down into manageable components that can be evaluated independently and systematically.
Solution Approach 2:
The system uses multiple parameters including learning state, progressive experience, and impact factor to comprehensively assess expertise. By changing and combining multiple assessment parameters rather than relying on a single metric, the system achieves more precise expertise evaluation.
2Measurement precision
If comprehensive individual information is collected and analyzed, then the accuracy of expertise profiling is improved, but the amount of information processing increases
Solution Approach 1:
The system performs preliminary actions by pre-defining the five expertise stages and their corresponding criteria before actual assessment. This preparation allows for more efficient processing of individual information during the profiling process, as the evaluation framework is already in place.
Solution Approach 2:
The system provides feedback by generating expertise profiles that include specific stage assignments and recommendations for improvement. This feedback mechanism enables continuous refinement of the assessment process and allows individuals to understand their current standing, reducing the need for repeated comprehensive assessments.
3Ease of operation
If expertise profiles are generated without AI assistance, then the simplicity of the process is maintained, but the quality of expertise evaluation deteriorates
Solution Approach 1:
The system enables self-service by allowing individuals to input their own information and receive automated expertise profiles. The machine learning models process the data autonomously, generating comprehensive evaluations without requiring manual intervention, thus maintaining ease of operation while improving evaluation quality.
Solution Approach 2:
The patent replaces manual mechanical assessment processes with automated machine learning models. This substitution eliminates the need for human evaluators to manually analyze and score expertise, thereby simplifying the operation process while significantly improving the consistency and quality of evaluation through algorithmic processing.
4Adaptability or versatility
If recommendations for overcoming inadequacy are provided, then the usefulness of the system is improved, but the complexity of the output increases
Solution Approach 1:
Instead of only providing a static expertise profile, the system inverts the approach by actively identifying gaps and providing actionable recommendations for improvement. This inversion adds value by transforming the profile from a descriptive tool into a prescriptive guide for development.
Solution Approach 2:
The recommendations are segmented by expertise stage, with specific guidance provided for each stage (data stage, information stage, skill stage, knowledge stage, wisdom stage). This segmentation makes the complex output more manageable and actionable, as users can focus on specific stages where improvement is needed.
Data Source
AI summary
The present disclosure provides a method of facilitating modeling expertise of individuals. Further, the method may include receiving individual information and analyzing the individual information. Further, the method may include generating an expertise profile of the expertise of the individual using one or more machine learning models based on the analyzing. Further, the expertise profile characterizes the expertise of the individual based on each of a learning state, a progressive experience, and an impact factor. Further, the expertise profile includes a value corresponding to each of the learning state, the progressive experience, and the impact factor. Further, the value includes one of a data stage, an information stage, a skill stage, a knowledge stage, a wisdom stage, and an enlightenment stage. Further, the one or more machine learning models may be trained corresponding, the progressive experience, and the impact factor using individual information.