Financial Search Interface for Context-Aware Text Extraction
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Solution Overview
Problem
Conventional search engines struggle to efficiently find specific textual information within financial documents, missing synonyms and alternative expressions, and fail to provide context-aware and sentiment analysis, making it difficult for financial analysts to evaluate the impact of text on stock prices.
Innovation Solution
A deep search system that utilizes a context extraction engine to recognize semantically defined scenarios, analyze sentiment, and classify text based on linguistic rules, enabling granular search and analysis of financial documents for sentiment and impact on stock prices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional web search engines are used to search for keywords in financial documents, then the search process is simple and fast, but the search results miss synonyms and alternative expressions, reducing measurement precision
Solution Approach 1:
The patent introduces an intermediary layer between the user query and the document search. This intermediary includes a thesaurus database and synonym expansion module that automatically generates alternative search terms from the user's keywords before searching documents, thereby capturing synonyms and alternative expressions without requiring the user to manually enter multiple search terms
Solution Approach 2:
The system performs preliminary actions by pre-processing and indexing financial documents with extracted entities, relationships, and contextual information before search queries are submitted. This pre-processing includes identifying financial concepts, their relationships, and contextual meanings, which enables more accurate search results when queries are submitted
2Loss of time
If traditional search engines return links to entire documents, then the search process is simple, but it takes too much time for analysts to review entire documents to find specific information, increasing loss of time
Solution Approach 1:
The patent extracts and highlights specific relevant portions, sentences, or phrases from within financial documents that match the search query. Instead of returning entire document links, the system extracts and presents only the pertinent segments containing the searched information, allowing analysts to quickly locate specific details without reviewing entire documents
Solution Approach 2:
The system segments financial documents into meaningful units such as sentences, paragraphs, or thematic sections during indexing. When a search query is submitted, results are presented as segmented excerpts rather than whole documents, enabling analysts to quickly scan and identify relevant information without reading complete documents
3Adaptability or versatility
If conventional search engines are used, then the system is simple to operate, but it cannot provide sentiment analysis or context-aware search, reducing adaptability
Solution Approach 1:
The patent implements a multi-functional search system that combines multiple capabilities into a single platform: keyword search, synonym expansion, entity recognition, relationship analysis, sentiment analysis, and contextual filtering. This universal system handles diverse search needs (fact-based queries, sentiment analysis, relationship exploration) through a unified interface, making the complex system appear simple to users while providing advanced adaptability
4Measurement precision
If text search software is used to find specific keywords, then the search is straightforward, but it misses synonyms and alternative expressions that the user was not able to think about, reducing measurement precision
Solution Approach 1:
The system employs an intermediary thesaurus and synonym expansion module that automatically generates alternative search terms from the user's keywords. This intermediary layer expands the search query to include synonyms, related concepts, and alternative expressions before searching documents, ensuring comprehensive coverage of relevant information without requiring the user to manually brainstorm multiple search terms
Solution Approach 2:
The system incorporates feedback mechanisms where search results and user interactions inform future synonym expansions and search term suggestions. The system learns from search patterns and user behavior to refine and expand synonym lists, improving the completeness of search results over time while maintaining automated operation
Data Source
AI summary
A method for rendering context based information on a user interface includes receiving a user request to extract the context based information from a database. The database includes a plurality of documents and the request includes at least one search criteria required to determine a context of the user request. The method includes generating a list of documents corresponding to the context of the user request and rendering on a viewing portion of the user interface the list of documents corresponding to the context of the user request.