Dynamic Context Extraction via Entropy-Based Windowing
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Solution Overview
Problem
Conventional analytics systems struggle to accurately detect nuanced contextual undertones such as emotions and sentiments in electronic documents due to their fixed-input size analysis, leading to misinterpretation of linguistic nuances and errors in automatic transcriptions.
Innovation Solution
The approach involves dynamic analysis of variably-sized portions of electronic documents using calculated entropies and entropy scores to identify shifts in contextual undertones, employing a 'shifting window' analysis and neural networks to determine emotional confidence scores and select representative token sets for accurate tagging of emotions and sentiments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional analytics systems use fixed-input size analysis, then the analysis process is simple and fast, but the accuracy of detecting contextual undertones and linguistic nuances deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from fixed-input size analysis to dynamic window analysis, where the analysis window size adjusts based on the detected entropy scores. The system dynamically determines optimal window sizes to capture contextual variations while maintaining computational efficiency, resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The system changes the parameter of input size from fixed to variable based on entropy calculations. By calculating entropy scores for different window sizes and selecting the optimal size, the system adapts the analysis parameters to match the contextual complexity of the text, improving detection accuracy without excessive complexity.
2Reliability
If conventional analytics systems analyze text with fixed input size, then processing is efficient, but the detection of emotion transitions and sarcasm deteriorates
Solution Approach 1:
The system uses dynamic window sizing that adapts to the emotional and contextual complexity of different text segments. High-entropy regions (indicating potential emotion transitions or sarcasm) receive larger analysis windows, while low-entropy regions use smaller windows, maintaining reliability for detecting nuanced emotions while preserving processing efficiency through selective analysis depth.
Solution Approach 2:
The patent applies local quality by treating different portions of text with different analysis intensities based on their local entropy characteristics. Regions with high entropy (potential emotional transitions) are analyzed more thoroughly, while stable regions receive lighter analysis, optimizing both detection reliability and processing efficiency locally across the document.
3Measurement precision
If conventional systems use fixed-size analysis windows, then the system is simple to implement, but accuracy in identifying contextual shifts deteriorates
Solution Approach 1:
The system implements parameter changes by using entropy-based metrics to dynamically adjust analysis window sizes. The implementation complexity is managed through algorithmic automation of the entropy calculation and window size selection process, allowing the system to achieve high accuracy in identifying contextual shifts without requiring complex manual configuration or processing.
Data Source
AI summary
Technology is disclosed for providing dynamic identification and extraction or tagging of contextually-coherent text blocks from an electronic document. In an embodiment, an electronic document may be parsed into a plurality of content tokens that each corresponds to a portion of the electronic document, such as a sentence or a paragraph. Employing a sliding window approach, a number of token groups are independently analyzed, where each group of tokens has a different number of tokens included therein. Each token group is analyzed to determine confidence scores for various determinable contexts based on content included in the token set. The confidence scores can then be processed for each token group to determine an entropy score for the token group. In this way, one of the analyzed token groups can be selected as a representative text block that corresponds to one of the plurality of determinable contexts. A corresponding portion of the electronic document can be tagged with a corresponding context determined based on the analyzed content included therein, and provided for output.