Form Field Autocomplete Using Dynamic ML Threshold Selection
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Solution Overview
Problem
Existing data entry systems are time-consuming and prone to errors, and current autocomplete systems struggle to accurately predict relationships between form fields and suggest likely values.
Innovation Solution
A system utilizing a dynamic threshold mechanism with machine learning models to auto-complete user input fields, adjusting precision and recall based on the stage of input and user feedback to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual data entry is used, then data entry can be performed with simple systems, but data entry is time-consuming and prone to errors
Solution Approach 1:
The patent replaces manual mechanical data entry with an automated machine learning-based autocomplete system. The system uses trained ML models to predict and auto-fill form fields, substituting human manual input with automated intelligent prediction, thereby increasing productivity while managing complexity through specialized algorithms rather than general-purpose tools
Solution Approach 2:
The autocomplete system performs self-service by automatically predicting and filling form fields without requiring manual user input for each field. The machine learning models independently analyze form relationships and generate completions, allowing the system to serve itself rather than requiring continuous human intervention for data entry tasks
2Productivity
If autocomplete systems are developed to suggest likely values, then data entry efficiency can be improved, but accuracy of suggested completions becomes difficult to ensure
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the classification threshold based on the stage of form completion. The threshold is not fixed but adapts according to how many fields have been completed, allowing the system to optimize between exploration (lower threshold) and exploitation (higher threshold) phases, thereby improving prediction accuracy at different stages of the data entry process
Solution Approach 2:
The system introduces dynamics by making the classification threshold variable rather than static. The threshold dynamically changes based on the completion stage, transitioning from a lower threshold in early stages (when exploration is beneficial) to a higher threshold in later stages (when precision is more important), enabling the system to adapt to changing requirements throughout the data entry process
3Measurement precision
If machine learning models are used to determine field relationships, then prediction accuracy can be improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the form completion process into distinct stages (e.g., initial stage, intermediate stage, final stage). Each stage has its own classification threshold, segmenting the prediction process into phases with different accuracy requirements. This segmentation simplifies the overall system by breaking down the complex prediction task into manageable stages with specific focus areas
Solution Approach 2:
The system manages model selection complexity by changing the classification threshold parameter based on the completion stage rather than using multiple complex models. This parameter-based approach simplifies the system architecture compared to maintaining separate models for different stages, while still achieving stage-appropriate prediction accuracy through dynamic threshold adjustment
Data Source
AI summary
As described herein, a system, method, and computer program are provided for using a dynamic threshold mechanism that is utilizing a set of machine learning models to auto-complete user input fields. User access to a form having a plurality of user input fields is detected. One or more of the plurality of user input fields are auto-completed over a sequence of stages, utilizing at least one machine learning model.