Document Analysis Module for Region Classification

Solution Overview

Problem

Converting books, especially those in PDF format, into electronic formats like MOBI for viewing on various devices often results in a costly and time-consuming manual process of identifying and classifying regions such as paragraphs, footnotes, and chapter headings, which hinders user navigation and accessibility.

Innovation Solution

A document analysis module that automatically identifies regions and region types in electronic books by using multiple sets of rules, typographical feature analysis, cluster analysis, and user input corrections to iteratively refine region classification, matching page layouts to template pages for accurate identification.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual process is used to identify and classify regions in converted books, then region identification accuracy can be achieved, but the process becomes costly and time-consuming

Engineering Contradiction:

Improveregion identification accuracyVSAvoidtime-consuming process

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs self-service by automatically analyzing page layouts and identifying regions without human intervention. The document analysis module autonomously processes converted books, applies classification rules, and generates region annotations, eliminating the need for manual region identification while maintaining accuracy through iterative rule refinement based on user feedback.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual process of region identification with an automated computational system. The document analysis module uses algorithmic rule-based classification and pattern recognition to substitute human manual analysis, dramatically reducing time consumption while preserving identification accuracy through sophisticated rule sets and user feedback mechanisms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If manual process is used to identify and classify regions in converted books, then region classification can be achieved, but the process becomes costly

Engineering Contradiction:

Improveregion classification accuracyVSAvoidcost-effectiveness

Core Design Contradiction:

Measurement precisionVSEase of manufacture

Solution Approach 1:

The system performs self-service by automatically analyzing page layouts and identifying regions without human intervention. The document analysis module autonomously processes converted books, applies classification rules, and generates region annotations, eliminating the need for manual region identification while maintaining accuracy through iterative rule refinement based on user feedback.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual process of region identification with an automated computational system. The document analysis module uses algorithmic rule-based classification and pattern recognition to substitute human manual analysis, dramatically reducing time consumption while preserving identification accuracy through sophisticated rule sets and user feedback mechanisms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If automated rule-based system is used for region identification, then processing speed increases, but accuracy may decrease without user corrections

Engineering Contradiction:

Improveprocessing speedVSAvoidregion identification accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system implements feedback mechanisms where user corrections and annotations are captured and used to refine classification rules. The document analysis module learns from user feedback, adjusting rule parameters and thresholds to improve accuracy over time while maintaining high processing speeds through automated rule-based classification.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The rule sets in the system are dynamic and adaptable rather than static. The classification rules evolve based on user feedback and correction data, allowing the system to maintain high processing speed while continuously improving accuracy. The system dynamically adjusts rule parameters, thresholds, and classification logic based on accumulated user interactions and correction patterns.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS10013488B1Document analysis for region classification

Publication Date: 2018.07.03 AMAZON TECH INC

AI summary

A document analysis module analyzes electronic media items and identifies regions and region types for the electronic media items. The document analysis module may use rules, typographical feature sets, and cluster analysis to identify regions and region types. The document analysis module may also receive user input and may use the user input to identify regions and region types. The document analysis module may further use template pages to identify regions and region types.