Entity Classification Using Machine Learning Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for entity classification, such as hiring processes, are prone to errors, subjective, and resource-intensive, especially when dealing with large volumes of resumes, as they rely on manual processing and lack efficient algorithms to accurately identify qualified candidates.
Innovation Solution
An analytics platform uses machine learning techniques to receive information about job requirements, identifies priority and ancillary terms from positive and negative entity data, generates models, and determines classification scores for unclassified entities, thereby improving the accuracy and efficiency of the classification process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processing methods are used for entity classification, then human judgment and flexibility are maintained, but the process becomes resource-intensive and time-consuming when dealing with large volumes of data
Solution Approach 1:
The patent replaces manual mechanical processing with an automated machine learning system that uses natural language processing and classification algorithms to analyze entity data, extract features, and generate classification scores, thereby maintaining accuracy while dramatically improving processing speed and scalability
Solution Approach 2:
The patent introduces intermediary components including feature extractors, model generators, and score calculators that bridge the gap between raw entity data and final classification results, enabling systematic automated processing while preserving the nuanced judgment capabilities through trained machine learning models
2Ease of operation
If manual processing methods are used for entity classification, then subjective human judgment is maintained, but the process lacks efficient algorithms and becomes error-prone
Solution Approach 1:
The patent transforms subjective human judgment into objective measurable parameters by extracting quantitative features from entity data and using machine learning models to generate consistent classification scores based on these parameters, eliminating variability while maintaining operational simplicity through automated workflows
Solution Approach 2:
The patent creates reusable classification models that capture expert judgment patterns and can be repeatedly applied to new entity data without requiring manual intervention, ensuring consistent and reliable classification results across different datasets and time periods
3Measurement precision
If comprehensive entity data is analyzed to improve classification accuracy, then the quality of classification scores improves, but the computational resources and processing time increase
Solution Approach 1:
The patent extracts only the most relevant features from comprehensive entity data using natural language processing and feature selection techniques, maintaining high classification accuracy by focusing on discriminative features while reducing the volume of data that requires computational processing
Solution Approach 2:
The patent segments the classification process into distinct stages including data preprocessing, feature extraction, model generation, and score calculation, allowing computational resources to be efficiently allocated to each stage and enabling parallel processing where applicable
Data Source
AI summary
A device may receive information that identifies a requirement. The device may receive information associated with a set of positive entities and a set of negative entities. The device may identify a set of priority terms based on the information associated with the set of positive entities and the set of negative entities. The device may identify a first set of ancillary terms and a second set of ancillary terms based on the information that identifies the set of priority terms. The device may generate a model based on the set of priority terms, the first set of ancillary terms, and the second set of ancillary terms. The device may determine a set of classification scores, for a set of unclassified entities, based on information associated with the set of unclassified entities and the model. The device may provide information that identifies the set of classification scores to cause an action to be performed in association with the set of unclassified entities.