Financial Search Interface With Context and Sentiment Filtering
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Solution Overview
Problem
Conventional search engines fail to efficiently identify relevant financial documents by missing synonyms and alternative expressions, and do not account for contextual relevance, sentiment, or temporal significance, 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 classify text 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 web search engines return links to entire documents in response to keyword searches, then the search process is simple, but the search precision and contextual relevance are poor
Solution Approach 1:
The patent segments documents into sentences and phrases, then searches at the sentence level rather than returning entire documents. The system divides the search task into: (1) extracting sentences containing search terms, (2) analyzing sentiment of those sentences, (3) evaluating contextual relevance. This segmentation improves precision by focusing on specific relevant portions rather than entire documents.
Solution Approach 2:
The patent introduces an intermediary sentiment analysis component between the search engine and the user. This intermediary analyzes the sentiment of sentences containing search terms and filters results based on sentiment criteria, acting as a mediator that enhances search precision without requiring complete system redesign.
2Productivity
If traditional search engines search for exact keywords, then the search operation is fast, but the ability to find synonyms and alternative expressions is poor
Solution Approach 1:
The patent implements dynamic search by allowing users to input multiple synonyms and alternative expressions for the same concept. The system dynamically adapts the search query to include these variations, enabling efficient searching across multiple semantic equivalents without manually executing separate searches for each term.
Solution Approach 2:
The search system is designed to handle multiple types of search inputs simultaneously - exact keywords, synonyms, alternative expressions, and sentiment criteria - all within a single unified search operation. This multi-functionality allows the system to maintain efficiency while adapting to various search needs.
3Loss of information
If search engines return all documents containing keywords, then the recall is high, but the information overload and difficulty in finding relevant information increases
Solution Approach 1:
The patent applies preliminary filtering by sentiment criteria before presenting results to the user. The system pre-analyzes the sentiment of sentences containing search terms and filters out sentences that do not match the desired sentiment (positive, negative, or neutral), reducing information overload before the user sees the results.
Solution Approach 2:
The system applies different quality criteria to different parts of the search results. Instead of uniformly treating all documents, it evaluates each sentence individually for sentiment and contextual relevance, presenting only those local portions (sentences) that meet the specified criteria, thereby maintaining information completeness while improving ease of operation.
4Measurement precision
If analysts manually review each document to assess sentiment and context, then the analysis is thorough, but the time consumption increases significantly
Solution Approach 1:
The patent implements automated sentiment analysis that performs the analytical work independently without requiring manual reviewer intervention. The system automatically extracts sentences, analyzes their sentiment, evaluates contextual relevance, and presents filtered results, enabling thorough analysis without proportional increase in time consumption.
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.