Machine Learning Autofill for Diverse Web Forms
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Solution Overview
Problem
Conventional methods for filling out web forms are inefficient, as they often require users to repeatedly enter the same information across different websites, and existing automated solutions are inadequate in handling diverse web forms.
Innovation Solution
A system employing machine learning techniques to automatically fill web form fields by learning correlations between user data and form fields, using databases and instrumented tools to collect and analyze user input, and applying constraints to ensure accurate data mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methods are used to remember previous entries, then user time is saved on identical forms, but the system cannot handle diverse web forms with different structures
Solution Approach 1:
The system employs machine learning algorithms that automatically learn and adapt to different form structures without requiring manual programming or user intervention. The browser autonomously analyzes form patterns, collects training data, and improves its autofill capabilities across diverse forms, enabling it to serve itself in handling various form types effectively
Solution Approach 2:
The system dynamically adjusts its behavior based on learned parameters from training data. By changing its internal mapping parameters and correlation models based on observed form patterns, the system adapts to handle diverse form structures while maintaining high accuracy in autofilling user information
2Productivity
If automated input methods are implemented, then data entry speed increases, but accuracy and user needs satisfaction remain insufficient
Solution Approach 1:
The system incorporates feedback mechanisms where user corrections and manual inputs are captured and used to refine the machine learning models. This feedback loop continuously improves the accuracy of autofill operations while maintaining high speed, as the system learns from actual user behavior and adjusts its predictions accordingly
Solution Approach 2:
The system performs preliminary actions by pre-learning form patterns and user preferences during idle periods or through collected training data. This preliminary learning enables the system to provide accurate autofill suggestions before the user actually needs to fill out forms, ensuring both speed and accuracy are optimized in advance
Data Source
AI summary
The present invention provides a unique system and method that can employ machine learning techniques to automatically fill one or more fields across a diverse array of web forms. In particular, one or more instrumented tools can collect input or entries of form fields. Machine learning can be used to learn what data corresponds to which fields or types of fields. The input can be sent to a central repository where other databases can be aggregated as well. This input can be provided to a machine learning system to learn how to predict the desired outputs. Alternatively or in addition, learning can be performed in part by observing entries and then adapting the autofill component accordingly. Furthermore, a number of features of database fields as well as constraints can be employed to facilitate assignments of database entries to form values—particularly when the web form has never been seen before by the autofill system.