Machine Learning Model for Secure Document Request Completion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Requesting users face challenges in determining the factors causing recipient users to fail or delay in completing secure digital document requests, leading to inefficient workflows.
Innovation Solution
A secure document service uses machine learning to analyze features associated with secure documents and user characteristics, identifying potential failure probabilities and generating feature modification activities to improve request completion likelihood.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If requesting users send secure document requests with multiple tasks to recipient users, then the completeness and complexity of the document processing is improved, but the likelihood of recipient users failing to complete the request increases
Solution Approach 1:
The system performs preliminary analysis of request features using machine learning models before the recipient user receives the request. The model predicts completion probabilities and identifies features that may deter users from completing the request, allowing proactive modification of request features to improve completion likelihood while maintaining processing completeness
Solution Approach 2:
The system uses historical completion data to train machine learning models that provide feedback on which request features correlate with failure to complete. This feedback loop enables the system to identify problematic features (such as difficult or time-consuming tasks) and adjust request configurations to balance completeness with completion likelihood
2Measurement precision
If the secure document service analyzes multiple features and generates detailed modification activities, then the accuracy of predicting completion failure is improved, but the complexity of the system increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the secure document service and the feature analysis process. The model automatically processes multiple features and historical data to generate completion probability predictions, eliminating the need for complex manual analysis rules while maintaining high prediction accuracy
Solution Approach 2:
The machine learning model automatically analyzes request features, compares them against historical patterns, and generates modification activities without requiring manual configuration or complex system rules. The system self-optimizes by learning from historical completion data, reducing the complexity burden on the overall system architecture
Data Source
AI summary
A system and a method are disclosed for detecting that a requesting user is transmitting a request to a recipient user to perform one or more tasks with respect to a secure document. The system extracts features associated with the secure document and inputs the features into a machine learning model that outputs one or more probabilities corresponding to a potential failure of the request. Based on the one or more probabilities, the system generates one or more feature modification activities to improve a likelihood of the recipient user completing the request. The system provides the one or more feature modification activities to at least one of the requesting user and the recipient user.