User-Driven Contextualization for Document Query Systems
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Solution Overview
Problem
Existing systems for processing unorganized documents, such as insurance documents, struggle to provide flexible and adaptable contextualization, as they confine contextual adjustments to the development phase without user input.
Innovation Solution
A document processing platform that implements a User-Driven Contextualization Process, allowing users to upload documents, extract and auto-tag data, and interactively ask questions to receive contextualized answers, with the ability to refine context through question-answer pairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If contextual adjustments are confined to the development phase without user input, then the system structure is simpler, but the adaptability and flexibility of data interpretation are limited
Solution Approach 1:
The system transitions from static contextualization (fixed at development phase) to dynamic contextualization (continuously adjustable by users). Users can modify contextual parameters, add new context, and refine interpretations in real-time, making the system adaptable to changing needs without requiring complete system redesign.
Solution Approach 2:
The system implements feedback loops where user interactions with document interpretations generate data that refines future contextualization. User corrections, annotations, and preference inputs are fed back into the system to continuously improve contextual understanding and adjustment capabilities.
2Measurement precision
If users can interactively refine context through question-answer pairs, then the contextualization accuracy improves, but the processing time and system complexity increase
Solution Approach 1:
The system performs preliminary contextualization and auto-tagging of document elements before user interaction. This initial processing creates a baseline interpretation that users can then refine, rather than requiring users to build context from scratch through iterative questioning.
Solution Approach 2:
The system creates and maintains multiple versions or layers of contextualization - an initial automated version and refined versions based on user input. Users can work with copies of the document and context that they can modify without affecting the original, enabling efficient iterative refinement.
3Productivity
If automatic tagging and information prioritization are implemented, then user productivity improves, but the extent of automation increases system complexity
Solution Approach 1:
The system performs self-service through automatic tagging, classification, and prioritization of document elements. It autonomously identifies key information, extracts entities, and organizes content without requiring manual user intervention for these routine tasks, thereby improving productivity while managing automation complexity through modular design.
Data Source
AI summary
A document processing platform transforms unorganized documents into interactive systems where users can ask questions and get answers. The document processing platform implements a User-Driven Contextualization Process, which allows users to add meaning to data. The process also improves user productivity by automatically tagging information and allowing for the prioritization of relevant details. The user may then ask questions for which narrative answers are provided including the relevant details extracted from the document. The process of transforming an unorganized document into an interactive system where users can ask questions and receive answers is broken down into three phases including document upload and data extraction, user-driven contextualization and definition, and configuring the new documents for questions using a large language model such as OpenAI.