Electronic Form Progress Prediction Using Field Complexity Signals
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Solution Overview
Problem
Existing techniques for tracking user progress in completing electronic processes, such as forms, lack accuracy due to factors not evident in automated analysis, leading to inefficient use of processor resources and ineffective support interventions.
Innovation Solution
A machine learning model is trained to predict completion percentages and times for electronic processes using user-specific and field-specific data, including complexity and proficiency, and is continuously improved through a feedback loop.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing techniques base determination primarily on total number of fields completed, then automation extent is improved, but measurement precision deteriorates
Solution Approach 1:
The patent transforms the measurement parameters from simple field count to a weighted system incorporating field complexity scores and user proficiency levels. The machine learning model dynamically adjusts completion percentage calculations based on multiple parameters including field difficulty, user expertise, and actual time spent, thereby maintaining automation while significantly improving measurement precision.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw user interaction data and completion percentage determination. This intermediary processes complex inputs including field complexity metadata, user profile information, and interaction patterns to generate accurate completion assessments, resolving the contradiction between automated processing and precise measurement.
2Productivity
If automated analysis is used to track user progress, then productivity is improved, but measurement precision deteriorates due to factors not evident in automated analysis
Solution Approach 1:
The patent implements feedback loops where the machine learning model continuously learns from actual user completion data to refine its predictions. The system compares predicted completion percentages with actual outcomes, using this feedback to improve future accuracy assessments. This enables automated tracking to maintain both high productivity and improving measurement precision over time.
Solution Approach 2:
The patent performs preliminary analysis by pre-calculating field complexity scores and user proficiency levels before the actual completion process. This preparatory work enables the system to make more accurate real-time predictions during form completion, maintaining productivity while improving the precision of progress monitoring through pre-processed contextual information.
3Ease of operation
If interventions are provided without accurate progress monitoring, then ease of operation is improved, but loss of information occurs due to ineffective interventions
Solution Approach 1:
The patent enables targeted interventions by identifying specific fields or sections where users are struggling, rather than providing generic support. The machine learning model analyzes completion patterns at the field level, detecting anomalies such as excessive time spent on particular fields or repeated errors, and triggers localized interventions only where needed. This maintains ease of operation while preventing information loss by providing contextually appropriate support.
Data Source
AI summary
Aspects of the present disclosure relate to assisting users in completing forms. Embodiments include retrieving data associated with a form comprising a total number of fields of the form and an indication of a level of complexity of a field of the form. Embodiments further include retrieving data associated with a user comprising a number of fields of the form the user has completed and an amount of time the user has spent completing a respective field of the form. Embodiments further include providing the data associated with the form and the data associated with the user to a machine learning model, and predicting an amount of user progress in completing the form, or a an amount of time required for the user to complete a given field of the form. Embodiments further include receiving the prediction from the machine learning model.