Time Savings Prediction via ML and Image Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for estimating time saved by using automatic data import in tax and accounting applications are inaccurate and fail to account for real-world human behavior, leading to inconclusive results due to difficulties in measuring time differences between manual and automated entry methods across varying complexity and scenarios.
Innovation Solution
A system utilizing a machine learning algorithm trained on historical clickstream data and analytics to provide personalized time estimates for data entry tasks, considering factors such as form types and user behavior, to accurately predict time savings from using automatic data import versus manual entry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If automatic data import capabilities are used, then time required for data entry is reduced, but user adoption is low due to complexity of entering third-party credentials
Solution Approach 1:
The patent introduces an intermediary mechanism that automatically extracts data from images uploaded by users and populates the data entry forms without requiring manual credential entry. The system uses image processing technology as a mediator between the user's uploaded images and the required data fields, eliminating the need for users to manually enter third-party credentials while still achieving automatic data import functionality.
Solution Approach 2:
The system enables self-service by allowing users to simply upload images of their tax documents without requiring them to manually extract or enter data. The automated image processing system handles the complex data extraction and form population tasks automatically, making the process as easy as uploading an image while maintaining the time-saving benefits of automatic data import.
2Ease of operation
If manual data entry methods are used, then ease of operation is maintained, but time consumption increases significantly
Solution Approach 1:
The patent replaces the mechanical manual data entry process with an automated image processing system. Instead of requiring users to manually type data into forms, the system uses optical recognition and machine learning algorithms to automatically extract data from uploaded images and populate the forms, substituting manual mechanical actions with automated digital processing while maintaining ease of use.
3Device complexity
If existing time estimation methods are used, then simplicity is maintained, but accuracy is poor due to failure to account for real-world human behavior
Solution Approach 1:
The system incorporates feedback mechanisms by tracking actual user behavior during data entry tasks and using this information to refine and improve time estimation accuracy. The system monitors real-world factors such as user pauses, corrections, and review behaviors, then feeds this data back into the estimation model to continuously improve prediction accuracy while accounting for human behavioral variations.
Solution Approach 2:
The patent changes the parameters used in time estimation by incorporating multiple real-world factors such as document complexity, user expertise level, and behavioral patterns into the estimation model. Instead of using simple fixed-time estimates, the system dynamically adjusts time predictions based on varying parameters including the number of form fields, document image quality, and observed user interaction patterns, significantly improving prediction accuracy.
Data Source
AI summary
Systems and methods for quantifying saved time during data entry.