Financial Search Interface for Contextual Sentence and Sentiment Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional search engines struggle to efficiently find specific textual information within financial documents, missing synonyms and alternative expressions, and fail to analyze sentiment and context, making it difficult for financial analysts to make informed investment decisions.
Innovation Solution
A deep search system that utilizes a context extraction engine to recognize semantically defined scenarios, analyze sentiment and subjectivity, and classify sentences based on linguistic rules, enabling granular search and sentiment-aware analysis of financial documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines use keyword-based text search, then they can find specific keywords in financial documents, but they miss synonyms and alternative expressions that the user was not able to think about
Solution Approach 1:
The patent transforms the search parameter from simple keyword matching to semantic concept matching. By changing the search dimension from literal text to meaning-based concepts, the system can recognize synonyms and alternative expressions without requiring users to manually input multiple variations of search terms.
Solution Approach 2:
The patent introduces an intermediary layer (semantic analysis engine) between the user's search query and the document database. This intermediary translates keywords into semantic concepts and retrieves documents based on conceptual relevance rather than exact keyword matches, enabling synonym recognition while maintaining search precision.
2Quantity of substance
If conventional search engines return links to entire documents, then they provide comprehensive results, but they make it difficult for financial analysts to quickly find specific textual information within specific contextual topics
Solution Approach 1:
The patent extracts and highlights only the relevant sentences or paragraphs that contain the searched conceptual information, rather than returning entire documents. This extraction approach maintains information completeness by providing the specific textual evidence while eliminating unnecessary content, thereby reducing the time analysts need to search through documents.
Solution Approach 2:
The patent segments the search results into meaningful units (sentences or paragraphs) with contextual metadata, allowing analysts to quickly scan and identify relevant information without reading entire documents. The segmentation organizes information hierarchically from document level to sentence level, improving retrieval efficiency.
3Productivity
If traditional search engines search for keywords, then they can find documents containing those keywords, but they bring too many documents that contain the same keywords but in the wrong context
Solution Approach 1:
The patent changes the search parameter from keyword presence to contextual relevance. By analyzing the semantic context surrounding search terms and evaluating whether the keywords appear in appropriate contextual settings, the system maintains broad search coverage while filtering out irrelevant results that contain keywords but lack proper context.
4Ease of operation
If conventional search engines perform text search, then they can find specific keywords, but they do not allow the analyst to search for text that is either positive or negative from the perspective of the price of the company's stock
Solution Approach 1:
The patent makes the search engine multi-functional by integrating sentiment analysis capability into the search process. The same search interface that handles keyword queries also automatically analyzes and filters results by sentiment (positive, negative, neutral), allowing analysts to search for both keywords and sentiment simultaneously without needing separate tools.
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.