NLP Candidate Response Identification via Bitwise Confidence
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Solution Overview
Problem
Existing natural language processing systems face inefficiencies in identifying and understanding dynamic structures and changes within content, leading to inaccurate responses.
Innovation Solution
A system and method that applies natural language processing to analyze passages against input tokens, generating a structure to map matching content, assessing token alignments, and calculating a confidence value through bitwise operations to evaluate candidate passage responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language processing is applied to analyze passages against input tokens to identify matching content, then the accuracy of candidate response identification is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the natural language processing task into distinct components: tokenization of input and passage, generation of candidate responses, scoring of candidates, and selection of final responses. This segmentation allows each component to be optimized independently, managing computational complexity while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary actions by generating multiple candidate responses before final selection, and by pre-processing the passage and input tokens into structured formats. This preliminary processing organizes data in advance, making the subsequent scoring and selection more efficient despite the initial computational investment.
2Reliability
If multiple candidate responses are evaluated using closeness criteria and bitwise operations, then the reliability of the identified content is improved, but the loss of time increases
Solution Approach 1:
The patent evaluates multiple candidate responses beyond what might be strictly necessary, generating a set of scored candidates and selecting the top ones. This excessive action improves reliability through comprehensive evaluation, while the systematic scoring approach prevents complete analysis of every possible candidate, managing time loss.
Solution Approach 2:
The patent changes parameters by applying different scoring functions and closeness criteria to evaluate candidates. By adjusting these parameters and using bitwise operations for efficient comparison, the system achieves reliable identification without linearly increasing processing time for each additional evaluation dimension.
3Productivity
If a structure is generated to map matching content with focus and candidate answer entries, then the organization and evaluation efficiency are improved, but the device complexity increases
Solution Approach 1:
The patent segments the passage and candidate responses into discrete units with specific attributes (focus, candidate answer, score). This segmentation into structured entries improves evaluation efficiency by enabling systematic comparison, while the modular structure keeps complexity manageable through consistent formatting and organization.
Solution Approach 2:
The patent introduces an additional dimensional organization by mapping candidates to passages with multiple attributes (focus, score, closeness criteria). This multi-dimensional structuring improves evaluation efficiency by enabling filtering and sorting across different dimensions, while the systematic approach prevents exponential complexity growth.
Data Source
AI summary
Embodiments relate to an intelligent computer platform to identify and evaluate candidate passage response data in natural language form. Natural language processing is applied to analyze a passage against one or more input tokens to identify matching content. A structure representing the analyzed passage is populated with matching input and passage tokens. A first count of matching token entries and a second count of evaluated token entries are determined and qualified by closeness criteria. An alignment of the passage to a candidate question is calculated, including assessing a ratio of the first and second counts as a confidence value. Matching passage data is returned from the passage with the confidence value.