AI Candidate Evaluation System Using Self-Learning Models
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Solution Overview
Problem
Current methods for evaluating candidates through video communication are labor-intensive, require multiple evaluators, and often overlook critical non-core evaluation parameters like soft skills, due to the limited bandwidth and high cost of skilled evaluators.
Innovation Solution
A method and system utilizing self-learning AI models to extract video and audio features from candidate interviews in real-time, compare these features with self-adjusting threshold values, and generate scores for predefined parameters, thereby automating the evaluation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple evaluators are used for candidate assessment, then evaluation accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The patent creates a virtual copy of the evaluator through an AI system that replicates human evaluation capabilities. The AI model processes video and audio data to generate evaluation scores, effectively copying the evaluator's function without requiring multiple human evaluators to be present simultaneously, thus maintaining accuracy while reducing time consumption.
Solution Approach 2:
The patent replaces the mechanical system of human evaluators with an automated AI-based evaluation system. The system uses machine learning models to process interview data and generate assessments, substituting the manual mechanical process of human evaluation with an automated computational process that operates faster and at lower cost.
2Measurement precision
If multiple interview rounds are conducted, then comprehensive evaluation is achieved, but labor intensity and complexity increase
Solution Approach 1:
The AI evaluation system performs multiple evaluation functions simultaneously through a single interview round. The system analyzes various parameters including communication skills, technical knowledge, and soft skills in one pass, making the evaluation process universal and multi-functional rather than requiring separate specialized rounds for each skill type.
Solution Approach 2:
The patent merges multiple evaluation functions and parameters into a single integrated AI system that processes all aspects of candidate assessment simultaneously. Instead of separating evaluations into multiple rounds, the system combines analysis of video data, audio data, and multiple evaluation criteria into one unified process.
3Productivity
If evaluator bandwidth is increased to handle more candidates, then evaluation capacity improves, but cost and resource requirements increase
Solution Approach 1:
The AI evaluation system is self-service in nature, automatically processing candidate interviews without requiring human evaluators for each assessment. The system independently analyzes video and audio data, applies evaluation criteria, and generates scores autonomously, eliminating the need to proportionally increase human evaluator resources as candidate volume increases.
Solution Approach 2:
The patent changes the fundamental parameter of evaluation from human-based to AI-based processing. This parameter change enables the system to handle variable workloads without the linear resource increase required by human evaluators, as the AI system can process multiple candidates simultaneously or sequentially without additional cost or resource requirements.
Data Source
AI summary
This disclosure relates to method and system for evaluating through the Artificial intelligence (AI) model. The method includes receiving input data comprising video data and audio data corresponding to an interview of a candidate. The method further includes extracting in near real-time a set of video and audio features from each of the plurality of frames of the video data using a first self-learning AI model and audio data using a second self-learning AI model. The method further includes comparing the set of video features and the set of audio features with predefined threshold values corresponding to the set of predefined parameters. The method further includes generating a score corresponding to each of the set of predefined parameters of the candidate using the first self-learning AI model and the second self-learning AI model.