Smart Macros Using Zone Selection for Data Transfer
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Solution Overview
Problem
Users face inefficiencies and errors when transferring data between multiple digital documents of different formats, requiring repetitive and time-consuming processes that can lead to physical strain and increased errors due to the need to navigate and copy data across various documents.
Innovation Solution
A system and method that allows users to define moveable zones on input and target documents, capturing data within these zones and learning patterns to automatically propose fill arrangements for completing the target document, reducing the need for manual data transfer and minimizing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually transfer data between multiple documents by searching and copying individual cells, then data accuracy can be maintained, but the process becomes extremely time-consuming and physically straining
Solution Approach 1:
The system performs preliminary actions by automatically capturing data from source documents and pre-processing it before the user needs to transfer it. The system proactively identifies data relationships and prepares target document structures in advance, eliminating the need for manual searching and copying of individual cells while maintaining accuracy through automated data mapping.
Solution Approach 2:
The system enables self-service by automatically performing data transfer operations without requiring manual user intervention for each data point. The automated system captures data from source documents, learns patterns, and independently fills target documents, freeing users from repetitive manual copying while preserving data accuracy through systematic automation.
2Productivity
If users manually search for and copy data from large tables across multiple documents, then data can be transferred, but the complexity of navigation and data location increases significantly
Solution Approach 1:
The system introduces an intermediary layer that automatically handles the complex navigation between documents. This intermediary system captures data from source documents, processes relationships between data points, and maps them to appropriate locations in target documents, eliminating the need for users to manually navigate through large tables and multiple documents.
Solution Approach 2:
The patent replaces the mechanical manual navigation and copying process with an automated information processing system. Instead of users physically searching and copying cells, the system uses computational algorithms to automatically extract, process, and transfer data, substituting manual mechanical actions with automated digital processing.
3Reliability
If users perform repetitive manual data entry tasks, then data can be transferred to target documents, but physical strain and errors increase
Solution Approach 1:
The system performs self-service by automatically executing data transfer operations without requiring repetitive manual user actions. The automated system captures data from sources, learns patterns through observation of user interactions, and independently completes data entry tasks, eliminating physical strain while maintaining high accuracy through systematic automated processing.
Solution Approach 2:
The system incorporates feedback mechanisms where it observes and learns from user actions during the initial phase, then uses this feedback to refine and improve automated data transfer operations. This feedback loop ensures that the system adapts to specific data patterns and user needs, increasing accuracy while reducing the need for manual intervention.
Data Source
AI summary
A system and method for assisting a user to enter data are provided. The method includes detecting a user's selection of a moveable input zone on each of one or more input documents displayed on the user's screen and detecting a user's selection of a moveable target zone on a target document displayed on the user's screen. Data in the input zone(s) and the target zone is captured for one or more locations of the respective zones and a pattern is learned automatically for filling additional data in the target document based on the captured data. A fill arrangement for filling the additional data in the target document based on the learned pattern is generated, which can be presented to the user for validation.