Candidate Data Ranking via N-gram Audio Analysis
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Solution Overview
Problem
Current methods for selecting and sorting candidate data for open opportunities are inefficient, as they do not effectively compare candidate responses to specific prompts with those of successfully selected candidates, leading to suboptimal candidate selection.
Innovation Solution
A computerized method that evaluates candidate data by comparing textual, defined field, and audio responses to prompts with those of successfully selected candidates using N-gram analysis, weighting the results to prioritize similarity in candidate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional candidate selection methods are used, then the selection process is simpler, but the selection effectiveness and quality are reduced
Solution Approach 1:
The system performs preliminary actions by pre-processing audio data into N-gram datasets before the actual candidate evaluation. Successfully selected candidates' N-grams are stored in advance, creating a reference database that enables rapid comparison during the selection process, thereby improving reliability without proportionally increasing complexity
Solution Approach 2:
The patent creates N-gram copies of audio responses from successfully selected candidates and stores them in a database. These copied N-gram representations serve as reference patterns for comparing new candidates, enabling effective selection through pattern matching rather than direct audio comparison, thus improving selection quality while managing computational complexity
2Measurement precision
If detailed audio response comparison is performed, then candidate evaluation accuracy is improved, but processing time increases
Solution Approach 1:
The system extracts only the essential features from audio responses by converting them into N-gram datasets. This extraction process removes irrelevant audio characteristics while retaining the core linguistic patterns that distinguish successful candidates, achieving high comparison accuracy with reduced processing requirements and faster evaluation
Solution Approach 2:
The patent transforms audio data from its original temporal waveform representation into discrete N-gram frequency distributions. This parameter transformation changes the data representation from continuous audio signals to discrete textual patterns, enabling more efficient computational comparison while maintaining or improving evaluation precision
Data Source
AI summary
Various embodiments provide a method for sorting data. The method can include: receiving criteria data from a user regarding an open opportunity; receiving candidate data, storing candidate data in a candidate profile in a candidate database; evaluating the candidate data, wherein evaluating the candidate data comprises: comparing the audio data of the candidate to a database of successfully-selected candidates to determine an N-gram analysis result for the candidate data, wherein the N-gram analysis includes creating a textual transcript of the audio data for a portion audio data; removing stop words from the textual transcript; creating an N-gram data set based on the textual transcript with stop words removed; comparing the N-gram data set of the candidate data to an N-gram data set of the successfully-selected candidates; and sorting the candidate data within a plurality of other candidate data using the N-gram analysis result.