Sentence-Type Analysis for Precise Writer Fluency Feedback
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Solution Overview
Problem
Existing writing analysis systems fail to provide nuanced feedback on writer fluency and style, relying on generic metrics that do not accurately reflect a writer's skills or weaknesses, limiting their ability to improve.
Innovation Solution
A system and method that analyzes a piece of writing by identifying various sentence types and precision terms, aggregating data on their usage, and generating feedback to characterize writer fluency and suggest improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional writing analysis methods use generic sentence structure measures (simple, compound, complex, compound-complex), then the analysis can be performed with basic grammar rules, but the feedback becomes too generic to understand a writer's skill or weakness meaningfully
Solution Approach 1:
The patent segments writing analysis into multiple dimensions: sentence structure type, sentence function, clause relationships, and rhetorical devices. Each dimension is analyzed separately using specific rules and algorithms, then integrated to provide comprehensive feedback. This segmentation enables precise measurement of writing quality without requiring a single overly complex analysis system.
Solution Approach 2:
The patent transforms traditional binary grammar checking into a multi-parameter analysis system that evaluates sentence variety, structural complexity, functional diversity, and rhetorical effectiveness. By changing from simple pass/fail grammar checks to multiple continuous parameters, the system achieves higher measurement precision while maintaining manageable complexity through modular analysis components.
2Loss of information
If writing analysis focuses on basic grammar and spelling checks, then the system is easy to implement, but the feedback fails to provide meaningful insights into writer skill and areas for improvement
Solution Approach 1:
The patent adds new dimensions to traditional writing analysis by incorporating sentence function classification, clause relationship analysis, and rhetorical device detection alongside conventional grammar checking. This multi-dimensional approach recovers lost information about writer skill without requiring complete redesign of the analysis system, as each dimension builds upon the others.
Solution Approach 2:
The patent introduces intermediary analysis layers that translate raw text into structured data about sentence characteristics, then into meaningful feedback about writing skills. These intermediary representations (sentence type classifications, function tags, relationship graphs) act as mediators between the complex text and the simplified feedback, preserving information while managing complexity.
3Measurement precision
If the system provides detailed sentence-type specific feedback, then writer fluency can be characterized accurately, but the feedback generation becomes more complex
Solution Approach 1:
The patent performs preliminary classification of sentences into types and functions before generating feedback. By pre-categorizing sentences and identifying their characteristics in advance, the system prepares structured data that simplifies the subsequent feedback generation process. This preliminary action reduces the complexity of generating detailed fluency-specific feedback.
Solution Approach 2:
The analysis system generates its own feedback rules and thresholds based on the analyzed data patterns. Rather than requiring external configuration for each type of feedback, the system automatically determines appropriate feedback criteria from the corpus of analyzed writings, reducing the complexity of feedback generation while maintaining high precision in fluency characterization.
Data Source
AI summary
At least one computer readable medium encoded with instructions that, when executed on a computer system, perform a method for determining one or more fluency characteristics of a writer from a piece of their prepared text and providing data to enable characterisation of the writer fluency. The method includes the steps of receiving text data carrying information on the piece of writing to be characterised; enumerating a set of sentences in the piece of writing, wherein each sentence is identified by applying a set of stored rules to the text data; determining a sentence type for each sentence within the piece of text by relating data carrying information on each sentence to stored rules and/or type data; aggregating a data set dependent on the sentence types identified and dependent on a defined aggregation operation; Characterisation data carrying information writer fluency measured dependent on the data set is generated.