Automated Sentence Concreteness Conversion for Document Consistency

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Solution Overview

Problem

Existing documents often exhibit inconsistent concreteness levels across sentences, leading to reader confusion and increased research effort due to varying levels of detail, which can degrade document quality and consistency.

Innovation Solution

A system and method utilizing natural language processing and machine learning to evaluate and modify sentence concreteness levels automatically, preserving the general meaning while aligning with a target concreteness level, using a concreteness-level classification tree and conversion generator.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Stability of the object's composition

If natural language processing and machine learning are used to automatically evaluate and modify sentence concreteness levels, then document coherence and consistency are improved, but device complexity and processing time increase

Engineering Contradiction:

Improvedocument coherenceVSAvoidsystem complexity

Core Design Contradiction:

Stability of the object's compositionVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary system comprising a concreteness-level classification tree and a conversion generator that mediates between the original text and the modified text. This intermediary automatically evaluates sentence concreteness levels and generates proposed changes, resolving the contradiction by automating the complex NLP processes rather than requiring manual analysis, thus improving document coherence while managing system complexity through specialized AI components.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system changes the parameter of sentence concreteness level by using machine learning models to evaluate and modify individual sentences. The conversion generator specifically adjusts the concreteness parameter of sentences to match a target level, thereby improving overall document coherence and consistency without requiring complete rewriting of the document.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If manual evaluation and modification of sentence concreteness levels is performed, then processing accuracy is maintained, but productivity and efficiency decrease

Engineering Contradiction:

Improveconcreteness evaluation accuracyVSAvoidtext processing speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system enables self-service by allowing the conversion generator to automatically evaluate and modify sentence concreteness levels without requiring manual human intervention. The machine learning models perform the evaluation and generation tasks autonomously, maintaining measurement precision through trained algorithms while dramatically improving productivity by processing text at machine speed.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual process of evaluating and modifying sentence concreteness with an automated electronic system using natural language processing and machine learning. This substitution maintains accuracy through algorithmic consistency while increasing processing speed and productivity by eliminating manual labor bottlenecks.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Stability of the object's composition

If sentence concreteness levels are uniformly adjusted, then document quality and consistency are improved, but loss of original meaning and nuance may occur

Engineering Contradiction:

Improvedocument consistencyVSAvoidoriginal sentence meaning

Core Design Contradiction:

Stability of the object's compositionVSLoss of information

Solution Approach 1:

The system applies local quality by evaluating and modifying each sentence's concreteness level individually rather than uniformly adjusting the entire document. The conversion generator analyzes each sentence in context and makes targeted adjustments to achieve the desired concreteness level while preserving the local meaning and nuance of individual sentences, thereby improving document consistency without losing original intent.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system incorporates feedback mechanisms where the classification tree evaluates the concreteness level of each sentence and the conversion generator uses this feedback to propose appropriate modifications. The process allows for iterative refinement where the original meaning is preserved while adjusting concreteness, as the system continuously monitors and adjusts based on the evaluated feedback from each sentence analysis.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12450430B2Automated identification of sentence concreteness and concreteness conversion

Publication Date: 2025.10.21 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A method, computer system, and a computer program product for text corpus concreteness modification are provided. A computer performs natural language processing to determine a concreteness level of a first individual sentence of a text corpus. The computer generates, based on the natural language processing, a proposed change of the individual sentence. The individual sentence with the proposed change includes a modified concreteness level and preserves a general meaning of the individual sentence. The computer transmits the proposed change for presentation of the proposed change.