Visual Element Indexing for Document Search Precision
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Solution Overview
Problem
Existing search engines return numerous irrelevant results due to inadequate keyword-based indexing and searching methods, making it difficult for users to find specific information without knowing relevant keywords and presenting results in a way that is not easily understandable.
Innovation Solution
A system that identifies and indexes visual elements within documents, such as paragraphs, tables, lists, and graphs, allowing users to search by visual element types and receive prioritized results in the same format, with optional advertisement integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If keyword-based indexing and searching is used, then the search system can process large quantities of data, but it returns numerous irrelevant results and reduces search precision
Solution Approach 1:
The patent segments documents into distinct visual blocks (paragraphs, tables, lists, graphs) and indexes each block type separately with its own metadata. This segmentation allows the search system to process large quantities of data by organizing it into manageable units while improving precision by matching search queries to specific visual block types rather than treating all text uniformly.
Solution Approach 2:
The patent changes the indexing parameters from simple keyword-based indexing to visual element-based indexing with multiple parameters including block type, visual characteristics, and contextual metadata. This parameter change enables the system to maintain high data processing capacity while significantly improving search precision by matching queries against multiple dimensional parameters rather than single keywords.
2Adaptability or versatility
If users search by keywords without knowing relevant terms, then the search system can accept any query, but it fails to return relevant results
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically generates visual element descriptions and suggestions from the document content itself. When users submit queries without knowing relevant keywords, the system analyzes the visual blocks and provides auto-completion suggestions, related visual element descriptions, and contextual hints, allowing users to refine their queries based on what the system discovers in the documents.
Solution Approach 2:
The patent introduces visual element metadata and contextual descriptions as intermediaries between user queries and document content. These intermediaries translate user intent into structured search parameters by providing structured descriptions of visual blocks, enabling the system to bridge the gap between vague user queries and precise document matching without requiring users to know specific keywords.
3Loss of information
If search results are displayed as text summaries, then the system can provide information about found content, but it fails to present results in an easily understandable format
Solution Approach 1:
The patent creates visual copies of the original document blocks by rendering simplified versions of the actual visual elements (miniature tables, condensed graphs, formatted list previews) directly in the search results. This copying approach preserves the visual characteristics and structural information of the source content while making it compact and suitable for display in search result listings, allowing users to understand the nature and scope of found information at a glance.
Solution Approach 2:
The patent applies different display formats and visual characteristics to different types of search results based on their source block type. Tables are displayed with tabular formatting, lists with bullet points, graphs with visual representations, and paragraphs with appropriate styling. This local quality approach ensures that each result type is presented in the most understandable format for its specific content type, significantly improving ease of understanding.
Data Source
AI summary
A method for segmenting, identifying and indexing visual elements, and searching documents comprises for each document generating metadata, segmenting the document into blocks using the metadata, performing block operations on the identified blocks, identifying and indexing inline visual elements using data and metadata rules, identifying and indexing block visual elements using profiles, and searching for documents containing visual elements.