Cross-Form Field Association for Automated Data Population

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Populating multiple form fields with common data is time-consuming and tedious in administrative processes, such as opening a business or restaurant, due to the repetitive nature of completing similar fields across different forms.

Innovation Solution

A system utilizing machine learning to predict associations between fields in different forms and automatically populate or recommend populating associated fields based on user input, with user feedback for model improvement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual population of multiple form fields with common data is performed, then completeness of form filling is ensured, but time consumption and tediousness increase significantly

Engineering Contradiction:

Improvecompleteness of form fillingVSAvoidtime consumption

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables self-service by having the form filling process automatically populate fields based on detected associations between form fields. The machine learning model identifies relationships between fields and autonomously fills associated fields without requiring manual intervention, thus ensuring completeness while reducing time consumption.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual filling process with an automated machine learning-based system. The ML model analyzes field associations and automatically populates form fields, substituting human manual operations with an intelligent automated system that maintains accuracy while significantly reducing time investment.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Loss of time

If machine learning automation is implemented to populate form fields, then time consumption is reduced, but system complexity increases

Engineering Contradiction:

Improvetime consumptionVSAvoidsystem complexity

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The machine learning model acts as an intermediary between the user's input data and the form field population process. It mediates by detecting associations between fields and translating user input into automated field population actions, thereby reducing manual effort while managing system complexity through a centralized intelligent layer.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system incorporates feedback mechanisms where the machine learning model continuously learns from form filling patterns and associations. This feedback loop allows the system to improve its field association detection accuracy over time, managing complexity through adaptive learning rather than requiring overly complex initial system design.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12608536B2Using data submitted for a field to populate a different, associated field

Publication Date: 2026.04.21 ORACLE INT CORP

AI summary

Techniques for populating the fields of a form are disclosed. A machine learning model may be trained to predict associations between fields. The trained machine learning model may be applied to a plurality of forms to predict an association between a first field in a first form type and a second field in a second form type. Upon receiving a value for a first field in a first form of a first form type and based on the predicted association, the system may populate a second field of a second form of the second form type based on the value.