User Capability Ranking via Contextual Embeddings
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Solution Overview
Problem
Current technologies in natural language processing struggle to efficiently process large amounts of position-specific and applicant-related textual information, failing to maintain contextual understanding and semantic nuance, which is crucial for effectively ranking candidates for specific positions.
Innovation Solution
The system employs a combination of machine learning techniques, including a Longformer model, a NER tagging system, a cosine similarity calculator, and a large language model (LLM), to process capability data and requirements data. This system generates contextual embeddings, calculates semantic alignment scores, and produces capability rankings that accurately reflect a user's correlation to a particular position.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current natural language processing technologies are used to process large amounts of textual information, then processing speed may be maintained, but contextual understanding and semantic nuance are lost
Solution Approach 1:
The system segments the processing task into multiple specialized components: Longformer model for contextual embedding, NER tagging system for entity extraction, cosine similarity calculator for matching, and LLM for summary generation. Each component handles a specific aspect of the processing, allowing efficient parallel operation while maintaining comprehensive contextual understanding.
Solution Approach 2:
The patent transforms the input textual data into different parameter representations at each processing stage - from raw text to contextual embeddings, then to entity tags, similarity scores, and finally to capability rankings. This parameter transformation enables efficient computation at each stage while preserving the semantic meaning throughout the pipeline.
2Quantity of substance
If traditional NLP systems process large textual documents, then processing volume increases, but accuracy in capturing semantic nature decreases
Solution Approach 1:
The system introduces multiple intermediary processing layers between the raw input text and the final output. The Longformer model creates contextual embeddings as an intermediary representation, the NER system extracts entities as another intermediary layer, and the cosine similarity calculator creates matching scores. These intermediaries preserve semantic information while enabling efficient processing of large volumes.
Solution Approach 2:
The patent replaces traditional mechanical text processing methods with advanced machine learning models. Instead of rule-based or keyword-matching systems, it uses transformer-based Longformer models and large language models that can capture semantic nuances automatically, achieving high accuracy without manual intervention.
3Measurement precision
If comprehensive candidate analysis is performed, then ranking accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing the capability data through the Longformer model to generate contextual embeddings and through the NER system to extract entities before the actual matching occurs. This preliminary structuring of data enables faster comparison and ranking operations in the subsequent stages.
Solution Approach 2:
The system applies multiple layers of analysis beyond what a single model could provide - combining contextual embeddings, entity extraction, similarity scoring, and LLM-based summary generation. This excessive action ensures comprehensive candidate analysis and high-ranking accuracy, with each layer adding value rather than redundancy.
Data Source
AI summary
A sets of capability data can be received by a computing device. The each set of capability data can describe a user. The computing device can apply an artificial intelligence model to the sets of capability data to generate sets of vectors individually representing a respective one of the sets of capability data. The computing device can generate summary vectors individually corresponding to a particular set of the sets of capability data. The computing device can receive requirement data associated with a particular asset. The computing device can generate an asset vector for the particular asset based on the requirement data. The computing device can determine similarity scores individually based on a comparison of a corresponding summary vector of the summary vectors to the asset vector. The computing device can generate a ranking of the users for the particular asset based on the similarity scores.