Digital Content Layout Encoding for Search Accuracy
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Solution Overview
Problem
Conventional digital content search techniques focus on what is included in the content, failing to address how the content is configured, leading to reduced efficiency and accuracy in locating digital content of interest.
Innovation Solution
Digital content layout encoding techniques generate a layout representation using machine learning, describing both spatial and structural aspects of elements within the content through a two-pathway pipeline, enabling the consideration of layout in search processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional keyword search techniques are used to locate digital content, then the search process is simple and fast, but the search accuracy is reduced because layout information is not considered
Solution Approach 1:
The patent segments the digital content analysis into two distinct pathways: a spatial pathway that processes layout information (positions, sizes, arrangements of elements) and a structural pathway that processes content semantics (text, objects, relationships). This segmentation allows the system to independently encode and process layout features without complicating the overall search architecture, thereby improving search accuracy while maintaining manageable system complexity.
Solution Approach 2:
The patent introduces layout representations as intermediary structures that bridge the gap between raw digital content and search queries. These representations encode spatial and structural layout information in a standardized format that can be integrated with conventional keyword search, enabling layout-aware search without requiring complete system redesign.
2Adaptability or versatility
If layout representation is generated using machine learning to describe spatial and structural aspects, then search functionality is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent generates layout representations in advance through automated machine learning processing, creating pre-computed spatial and structural encodings of digital content. These pre-generated representations are stored and can be quickly retrieved during search operations, avoiding the need for real-time layout analysis and thus reducing processing time during actual search while maintaining enhanced search functionality.
3Loss of information
If only content inclusion is analyzed in digital content search, then the search process is straightforward, but the ability to locate content based on configuration and layout is lost
Solution Approach 1:
The patent adds a new dimension to digital content search by incorporating spatial and structural layout information alongside traditional content analysis. The two-pathway architecture introduces spatial coordinates, element positions, and arrangement relationships as additional dimensions of analysis, enabling the system to search based on content configuration without significantly increasing overall analysis complexity through modular processing.
Data Source
AI summary
Digital content layout encoding techniques for search are described. In these techniques, a layout representation is generated (using machine learning automatically and without user intervention) that describes a layout of elements included within the digital content. In an implementation, the layout representation includes a description of both spatial and structural aspects of the elements in relation to each other. To do so, a two-pathway pipeline that is configured to model layout from both spatial and structural aspects using a spatial pathway, and a structural pathway, respectively. In one example, this is also performed through use of multi-level encoding and fusion to generate a layout representation.