Semantic Document Analysis for Change Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing document analysis technologies fail to effectively distinguish between relevant and irrelevant changes in a webpage's content, leading to unnecessary updates being reported to users.
Innovation Solution
A framework for time series analysis of semantic changes in documents is developed, which segments documents, generates semantic representations, and compares these representations over time to identify changes that deviate from expected behavior, allowing for the filtering out of irrelevant updates and focusing on content relevant to the main topic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If structural analysis methods (DOM structure, boilerplate detection) are used to detect changes, then changes can be detected, but the ability to distinguish relevant from irrelevant changes is insufficient
Solution Approach 1:
The document is segmented into multiple segments, and each segment is analyzed independently through semantic representation and time series analysis. This allows differentiation between segments that change relevantly versus irrelevantly, resolving the contradiction by enabling precise change detection at the segment level while maintaining overall document context.
Solution Approach 2:
The patent transforms document content into semantic representations (vectors) and analyzes changes in these representations over time using time series analysis. By monitoring semantic parameter changes rather than just structural changes, the system can distinguish between relevant content changes and irrelevant updates like advertisement changes.
2Quantity of substance
If all document changes are reported to users, then complete information is provided, but unnecessary updates increase noise and reduce user experience
Solution Approach 1:
The system extracts only the relevant changes from the complete set of document updates by analyzing semantic representations of segments over time. Segments whose semantic changes deviate from expected patterns are identified as relevant and extracted for user notification, while routine changes (like ad updates) are filtered out, reducing noise while maintaining information completeness.
Solution Approach 2:
The system continuously monitors semantic changes and compares them against expected change patterns learned from historical data. This feedback mechanism allows the system to dynamically adjust what constitutes a relevant change, filtering out predictable updates while maintaining notification of genuinely important changes.
3Measurement precision
If semantic analysis is performed on all segments, then relevant changes can be identified, but computational complexity increases
Solution Approach 1:
By dividing the document into segments and analyzing each segment's semantic representation independently through time series analysis, the system manages computational complexity through modular processing. Each segment can be processed in parallel, and the complexity is distributed across multiple smaller analysis units rather than analyzing the entire document as one large unit.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for semantic document analysis. In one aspect, methods include the actions of segmenting a document into segments; generating semantic representations, each corresponding to one of the segments; determining a corresponding segment score for one or more of the segments based on the corresponding semantic representation, such that each segment score represents a change in the corresponding segment; comparing each segment score to a threshold score, such that the threshold score represents an expectation of change for the document; and identifying segments having segment scores that indicate the change in the corresponding segment deviates from the expectation of change for the document based on the comparison.