Text Analytics Display with Significance Indicators
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Solution Overview
Problem
Existing methods fail to efficiently convey the significance and underlying motivations behind textual information, requiring substantial time and effort from readers to comprehend the importance and shifts in reasoning within texts.
Innovation Solution
A system that associates textual portions with significance indicators, such as motivational and rational vested interest values, inflection points, and emotional levels, which can be displayed overlayed, alongside, or integrated into the text using graphical waveforms or other visual cues, allowing readers to grasp the author's intent and mindset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional text reading methods are used, then complete understanding of text can be achieved, but substantial time and effort must be expended
Solution Approach 1:
The patent segments text into portions and associates each portion with significance indicators that categorize the author's mindset (e.g., motivational vested interest, rational vested interest, inflection points, emotional levels). This segmentation allows readers to quickly identify and focus on significant portions rather than reading every word sequentially, thereby reducing reading time while maintaining comprehension accuracy.
Solution Approach 2:
The patent introduces significance indicators as an intermediary layer between the raw text and the reader's comprehension. These indicators act as mediators that pre-process and annotate the text's meaning, motivations, and reasoning shifts, enabling readers to grasp the author's intent without expending substantial time on deep analysis of every textual element.
2Measurement precision
If conventional text reading methods are used, then complete understanding of text can be achieved, but the significance of different portions of text can be lost on the reader
Solution Approach 1:
By segmenting text and assigning specific significance indicators to each portion, the patent ensures that the significance information is preserved and highlighted. Readers can immediately identify which portions contain motivational interests, rational interests, inflection points, or emotional content, preventing loss of significance information while maintaining accurate comprehension.
Solution Approach 2:
The patent employs visual cues and graphical waveforms to represent different significance levels and types of information. This visual differentiation helps readers distinguish between various portions of text based on their significance, ensuring that important information is not lost and can be quickly identified through visual rather than purely textual analysis.
3Loss of time
If visual significance indicators are added to text, then reading time is reduced, but device complexity increases
Solution Approach 1:
The system introduces a text analysis component as an intermediary that automatically generates significance indicators from the input text. This mediator handles the complexity of analyzing author mindset, motivations, and reasoning shifts, while presenting simplified visual cues to the reader. The complexity is contained within the analysis component rather than requiring complex user interaction or display mechanisms.
Solution Approach 2:
The system performs self-service by automatically analyzing the text and generating appropriate significance indicators without requiring manual annotation or complex user configuration. The text analysis component autonomously identifies motivational interests, rational interests, inflection points, and emotional levels, reducing the need for complex user-side processing while achieving quick comprehension.
Data Source
AI summary
A system can receive text. The text can be divided into various portions. One or more significance indicators can be associated with each portion of text: these significance indicators can also be received by the system. The system can then display a portion of text and the associated significance indicators to the user.