Analytical Question Answering with Visual Data Interpretation
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Solution Overview
Problem
Current Question Answer (QA) systems struggle to effectively process and answer analytical questions that incorporate tables and charts, as they primarily rely on textual data and lack the capability to interpret and utilize data from visual representations.
Innovation Solution
A method and system that utilize natural language processing (NLP), computer vision, and pattern recognition to extract information from tables and charts, form mathematical equations, and solve them to provide answers in natural language, enhancing the capabilities of QA systems to handle questions with visual data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If QA systems rely primarily on textual data processing, then the system complexity remains manageable, but the system cannot effectively interpret and answer questions containing tables and charts
Solution Approach 1:
The patent introduces an intermediary module that acts as a bridge between the existing textual QA system and visual data sources. This intermediary extracts information from tables and charts, converts it into textual form, and feeds it to the existing QA system, thereby enabling visual data processing without fundamentally redesigning the entire system architecture
Solution Approach 2:
The system is divided into separate functional modules: one for extracting information from visual data sources, another for processing the extracted information, and the existing QA system module. This segmentation allows each module to specialize in specific tasks while maintaining overall system manageability
2Productivity
If QA systems integrate table and chart interpretation capabilities, then the system can answer more complex analytical questions, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary extraction and structuring of information from tables and charts before the actual QA processing begins. By pre-processing visual data into organized textual representations with extracted entities, relationships, and numerical data, the system reduces the computational burden during the actual question-answering phase
Solution Approach 2:
The patent extracts only the relevant information from tables and charts that is necessary for answering the specific question, rather than processing the entire visual data set. This selective extraction reduces the amount of data that needs to be processed and stored in memory
3Measurement precision
If QA systems use natural language analysis to extract information from tables and charts, then the system can provide accurate answers, but the system cannot handle questions requiring mathematical calculations
Solution Approach 1:
The patent merges the information extraction module with a mathematical problem-solving module. The extracted information from visual data is automatically fed into equation formation and solving capabilities, creating an integrated system that handles both qualitative analysis and quantitative calculations seamlessly
Solution Approach 2:
An intermediary component transforms extracted visual data into mathematical representations by forming equations based on the extracted information. This mediator converts non-mathematical extracted data into a format suitable for mathematical problem-solving
Data Source
AI summary
A method providing an answer to at least one analytical question containing at least one table or at least one chart is provided. The method may include receiving an input question. The method may also include extracting a plurality of information from the input question based on a natural language analysis. The method may further include forming a well-defined sentence. The method may include extracting at least one table or at least one chart associated with the input question. The method may include forming at least one mathematical equation. The method may also include solving the at least one mathematical equation. The method may include determining the answer to the input question in natural language based on the solved at least one mathematical equation. The method may further include narrating the determined answer to the input question in natural language.