Scroll-Triggered Web Form Completion with Preference Prediction

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Solution Overview

Problem

Manual entry of data into online form fields is time-consuming and inefficient, especially when multiple options are available, and existing technologies lack effective methods for automatic field completion based on user preferences.

Innovation Solution

A system that monitors scrolling behavior to detect form fields and automatically completes them using machine learning to infer user preferences, allowing users to scroll up or down to accept or change the completion, and provides confidence scores for uncertain options.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual entry of data into form fields is performed, then user control and accuracy are maintained, but time consumption and efficiency deteriorate

Engineering Contradiction:

Improvedata entry efficiencyVSAvoidautomatic field completion

Core Design Contradiction:

ProductivityVSExtent of automation

Solution Approach 1:

The system enables automatic field completion by monitoring scroll events and autonomously filling form fields based on detected locations, allowing the form to complete itself without requiring manual user input for each field

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system pre-fills form fields with suggested data before the user reaches them during scrolling, using machine learning models to predict and prepare completion options in advance based on user behavior patterns and contextual information

Inventive Principle:

Principle #10Preliminary action

2Productivity

If automatic field completion is implemented, then data entry efficiency is improved, but user control and accuracy deteriorate

Engineering Contradiction:

Improvedata entry efficiencyVSAvoidcompletion accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system provides feedback to users through confidence scores displayed next to auto-completed fields, allowing users to assess the reliability of each completion and correct errors if necessary, maintaining control while benefiting from automation

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system adjusts the level of automation dynamically based on confidence scores and user interactions, switching between automatic completion and manual input modes to maintain accuracy while maximizing efficiency

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If machine learning models are used to infer user preferences, then completion accuracy is improved, but system complexity deteriorates

Engineering Contradiction:

Improveuser preference inference accuracyVSAvoidsystem architecture

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The machine learning model acts as an intermediary component that processes user behavior data and contextual information to generate completion suggestions, separating the complexity of AI inference from the core form-filling functionality

Inventive Principle:

Principle #24Intermediary (Mediator)

4Speed

If real-time monitoring of scroll events is implemented, then response time is improved, but energy consumption and processing requirements deteriorate

Engineering Contradiction:

Improvefield completion response timeVSAvoidprocessing resource consumption

Core Design Contradiction:

SpeedVSUse of energy by moving object

Solution Approach 1:

The system monitors only specific events (scroll events) rather than continuously processing all page elements, and selectively triggers completion only when fields are visible and ready, reducing unnecessary processing while maintaining fast response times

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12405720B2Scroll-based automatic field completion

Publication Date: 2025.09.02 CAPITAL ONE SERVICES LLC

AI summary

Scroll-based automatic field completion is disclosed. A page displayed by a web browser can be monitored for empty fields as the page is scrolled. Automatic fill or completion of the empty field can be triggered in response to a request to scroll down based on the location of the empty field on the page. Automatic completion can involve determining a type of data associated with the field and retrieving user data of the type from a data store. A machine learning model can also be invoked to predict a user's preference for multiple completion options. Further, automatic refill of a completed field with different information can be initiated in response to a scroll up and the location of the field on the page.