Document Object Clustering by Semantic Context
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Solution Overview
Problem
Conventional data clustering techniques fail to effectively identify duplications among documents and provide a continuous flow of information, leading to inefficiencies and confusion in information retrieval due to the heterogeneous nature of multimedia content.
Innovation Solution
A method and system for clustering document objects based on semantic context, which identifies object chunks, determines document portions, and categorizes them into hierarchies to provide relevant information retrieval by matching user queries with categorized object chunks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional clustering techniques are used to organize data into clusters, then data can be grouped together, but information duplication occurs and retrieval efficiency deteriorates
Solution Approach 1:
The patent segments documents into granular object chunks based on semantic context rather than treating entire documents as units. This fine-grained segmentation allows the system to identify and group only the relevant information portions, preventing duplication while maintaining retrieval efficiency. Each object chunk is independently analyzed and clustered, enabling precise information organization.
Solution Approach 2:
The patent introduces an intermediary clustering mechanism that operates between the raw document data and the final retrieved information. This intermediary layer processes and reorganizes object chunks into coherent clusters, filtering out duplications and establishing a continuous flow of information that maintains both organization and retrieval efficiency.
2Reliability
If documents with overlapping information are clustered together, then related documents are grouped, but information duplication and confusion increase
Solution Approach 1:
The patent applies local quality analysis by examining the semantic context and information density of specific object chunks within documents. Instead of uniformly treating all document portions equally, the system identifies and prioritizes locally significant information segments, allowing related documents to be clustered while filtering out duplicative content through localized analysis.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor and adjust the clustering process. By analyzing the information content and overlap patterns of object chunks, the system receives feedback about duplication tendencies and automatically adjusts clustering decisions to maintain information relevance while preventing duplication.
3Adaptability or versatility
If dynamic clustering of multimedia content is performed, then data can be organized into bins, but the heterogeneous nature of data makes clustering challenging
Solution Approach 1:
The patent segments heterogeneous multimedia content into standardized object chunks, transforming diverse data types into a uniform representation that can be systematically clustered. This segmentation approach maintains adaptability to different data types while simplifying the clustering process through standardized processing of discrete objects.
Solution Approach 2:
The patent changes the parameters for clustering from traditional document-level metrics to object-chunk-level semantic context parameters. This parameter transformation enables the system to handle heterogeneous multimedia content uniformly by focusing on the semantic properties of individual objects rather than the heterogeneity of the entire data structure.
Data Source
AI summary
This disclosure relates to method, device, Wand system for clustering document objects based on information content. The method may include identifying a plurality of object chunks from at least one document based on semantic context of each of the plurality of object chunks, determining at least one document portion from the at least one document as a base document based on a plurality of parameters applied to the plurality of object chunks, determining a plurality of hierarchies within the base document, and categorizing the plurality of object chunks based on the plurality of hierarchies and information in each of the plurality of object chunks. It should be noted that each of the plurality of object chunks may include at least one object selected from the at least one document.