Semantic Abstraction Model for Digital Literature Comparison

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Solution Overview

Problem

Current methods for comparing digital literature are shallow, relying on genre and main plot elements, failing to consider deep semantic nuances and requiring human effort, which becomes unwieldy with the vast number of digitally published works, and do not meet customer expectations for instant access to similar literature.

Innovation Solution

Deep semantic analysis is performed to decompose digital literary works into multiple levels of abstraction, allowing for the identification of plot elements and user preferences, enabling automated comparison and recommendation of alternative works by converting unstructured text into structured annotations and generating multi-layer abstraction models.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If deep semantic analysis is performed on digital literature, then measurement precision of literary comparison is improved, but device complexity and loss of time increase

Engineering Contradiction:

Improveliterary comparison precisionVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments literary analysis into multiple hierarchical levels of abstraction, from detailed plot elements and characters at lower levels to thematic and stylistic patterns at higher levels. This segmentation allows the system to perform comprehensive deep semantic analysis by breaking down complex texts into manageable analytical components, improving measurement precision without overwhelming system complexity

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary action by pre-processing and structuring literary texts into standardized formats with identified plot elements, characters, and themes before comparison. This preliminary structuring of unstructured text into organized data models enables more efficient subsequent analysis and reduces the computational complexity of deep semantic comparisons

Inventive Principle:

Principle #10Preliminary action

2Productivity

If automated comparison of literary works is implemented, then productivity of recommendation system is improved, but measurement precision of user preference detection deteriorates

Engineering Contradiction:

Improverecommendation speedVSAvoiduser preference accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system implements dynamics by adaptively adjusting the depth and focus of semantic analysis based on user interactions and feedback. The comparison process dynamically prioritizes different levels of abstraction depending on user preferences demonstrated through ratings and behavior, enabling fast automated processing while maintaining high precision in detecting user preferences through iterative refinement

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system incorporates feedback mechanisms where user ratings and interactions with recommended literature feed back into the analysis model. This feedback loop continuously refines the measurement of user preferences by comparing actual user behavior against predicted preferences, improving accuracy over time while maintaining automated high-speed operation

Inventive Principle:

Principle #23Feedback

3Measurement precision

If manual comparison methods are used for digital literature, then measurement precision of literary analysis is improved, but loss of time and productivity deteriorate

Engineering Contradiction:

Improveanalysis accuracyVSAvoidcomparison time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system replaces manual mechanical analysis with automated computational semantic analysis. Natural language processing algorithms and semantic similarity calculations automatically perform the comparison tasks that would otherwise require human readers to manually analyze and compare literary works, maintaining high measurement precision through sophisticated linguistic analysis while eliminating time loss associated with manual processing of vast numbers of digital literature

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11048882B2Automatic semantic rating and abstraction of literature

Publication Date: 2021.06.29 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Deep semantic analysis is performed on an electronic literary work in order to detect plot elements and optional other storyline elements such as characters within the work. Multiple levels of abstract are generated into a model representing the literary work, wherein each element in each abstraction level may be independently rated for preference by a user. Through comparison of multiple abstraction models and one or more user rating preferences, one or more alternative literary works may be automatically recommended to the user.