Document Management System with ML Field Prediction
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Solution Overview
Problem
Conventional document management systems require manual creation and sending of documents, which is inefficient and lacks automation in generating content based on historical workflows.
Innovation Solution
A document management system uses a supervised machine learning model to predict values for fields in an electronic document, generating a user interface with suggested values for both confident and uncertain fields, allowing users to confirm and edit, thereby automating the document generation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual document creation is used, then users have full control over document content, but the process is inefficient and time-consuming
Solution Approach 1:
The system performs preliminary actions by predicting and pre-filling document fields based on historical workflows before the user completes the document creation process. This allows the system to prepare content in advance, reducing the time users need to spend on manual input while maintaining control over the final document.
Solution Approach 2:
The document management system provides self-service capabilities by automatically generating document content based on historical workflows and user input patterns. The system serves itself by using past data to predict and suggest current document values, reducing the need for manual intervention while maintaining user control.
2Measurement precision
If machine learning predicts values for document fields, then document generation accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the document fields into different categories (e.g., mandatory fields, optional fields, calculated fields) and applies machine learning predictions selectively to appropriate fields. This segmentation allows the system to maintain high prediction accuracy for relevant fields while avoiding unnecessary complexity in areas where manual input is still required.
Solution Approach 2:
The machine learning model acts as an intermediary between historical workflow data and current document generation. It processes past data patterns and translates them into predictive suggestions for current fields, bridging the gap between historical data and present needs without requiring direct complex interactions between all system components.
3Loss of time
If the system provides predicted values for all fields, then document completion speed increases, but user control and editing flexibility decrease
Solution Approach 1:
The system dynamically adjusts the level of automation based on user interaction. When users view predicted values, the system can adapt by providing more detailed information or allowing easier editing of specific fields. This dynamic behavior maintains speed benefits while preserving user flexibility according to real-time user needs.
Solution Approach 2:
The system applies different levels of prediction confidence and editing flexibility to different fields locally. Fields with high prediction confidence may be pre-filled with less editing required, while fields with lower confidence or higher user demand maintain full editing flexibility. This local differentiation optimizes both speed and user control where needed.
Data Source
AI summary
A document management system uses machine learning to generate electronic documents. Based on user input into a workflow for generating an electronic document, the document management system determines fields that require definition in the electronic document. The document management system predicts values for a first set of fields and inputs signals for a second set of fields into a supervised machine learning model, which is configured to output predicted values for the second set of fields. A user provides feedback on the predicted values of the second set of fields. The document management system incorporates the user's feedback into the generated electronic document, using confirmed values for each of the fields.