Personalized Document Field Prediction from User Feedback
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Solution Overview
Problem
Existing document processing technologies struggle with accurately extracting information from unstructured documents like invoices due to inconsistent user behavior and inefficiently consume computing resources, leading to excessive manual adjustments and network congestion.
Innovation Solution
A system that generates unique features from user documents and utilizes user behavior data to improve prediction accuracy, reducing manual input and network traffic by leveraging machine learning models to learn from historical interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standard NLP models are used to process documents, then general pattern recognition is achieved, but accuracy for user-specific patterns deteriorates
Solution Approach 1:
The system segments the document processing task into two distinct stages: (1) a base model that handles general document patterns using standard NLP, and (2) a personalized model that handles user-specific patterns. This segmentation allows each model to specialize in its respective domain, improving overall accuracy while maintaining adaptability to different user needs.
Solution Approach 2:
The system adds a new dimension to the processing architecture by introducing a personalized model layer that operates in parallel to the base model. This dimensional expansion enables the system to capture both general patterns (base model) and user-specific patterns (personalized model), resolving the contradiction between adaptability and precision.
2Measurement precision
If personalized models are trained for each user, then field extraction accuracy is improved, but computing resource consumption deteriorates
Solution Approach 1:
The system performs preliminary action by pre-training personalized models during off-peak hours or when resources are available, then storing these trained models for later use. This allows the heavy computational work to be done in advance, reducing real-time computing resource consumption while maintaining high extraction accuracy when documents are processed.
Solution Approach 2:
The system creates copies of trained personalized models and stores them in memory or cache. Once a personalized model is trained, multiple copies can be served simultaneously to handle multiple document processing requests, eliminating the need to re-train the model repeatedly and significantly reducing computing resource consumption during actual document processing.
3Measurement precision
If manual adjustments are made frequently, then prediction accuracy is improved, but time consumption and network traffic deteriorate
Solution Approach 1:
The system implements feedback mechanisms where user corrections to field extractions are automatically captured and used to retrain and update personalized models. This continuous feedback loop enables the system to learn from manual adjustments and automatically improve its predictions over time, reducing the frequency and time of manual adjustments needed while maintaining high accuracy.
Data Source
AI summary
Particular embodiments relate to personalized document field prediction based on user behavior and feature generation. Specifically, various embodiments have the technical effect of improved accuracy with respect to field/entity value prediction (e.g., predicting that the amount due is X via a Gradient Boosting Model) relative to document processing technologies by learning through user behavior data or feedback (e.g., through continuous reinforcement learning from human feedback (RLHF)). This is at least partially because of the technical solution of accessing or generating unique features from one or more documents previously used by a user.