Predictive Copy-Paste Modeling Across Patient Data GUIs
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Solution Overview
Problem
Managing patient information across multiple databases and graphical user interfaces (GUIs) is complex, time-consuming, and prone to errors due to repetitive manual data entry and navigation, with existing systems failing to automate the process of moving information and suggest autonomous tasks.
Innovation Solution
A patient management platform utilizing a machine learning model to analyze GUI fields and generate copy-paste operations, presenting prompts for user selection and automatically executing these operations to streamline data transfer between databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data entry and navigation is used to manage patient information across multiple databases and GUIs, then flexibility and adaptability are maintained, but time consumption increases and accuracy decreases
Solution Approach 1:
The system performs self-service by automatically detecting copy-paste operations and executing them without requiring manual user intervention. The machine learning model autonomously analyzes GUI fields, determines source and destination locations, and executes data transfer operations, thereby eliminating time-consuming manual navigation and data entry while maintaining high accuracy.
Solution Approach 2:
The patent replaces the mechanical manual process of copying and pasting data across multiple GUIs with an automated machine learning-based system. The machine learning model substitutes for human cognitive processing and manual operations, automatically identifying data sources, determining target locations, and executing transfers, thus reducing both time consumption and human error.
2Productivity
If automated copy-paste operations are implemented using machine learning, then time consumption and errors are reduced, but system complexity increases
Solution Approach 1:
The machine learning model acts as an intermediary between the user and the complex data management system. It provides a simplified interface by automatically analyzing GUI fields, determining copy-paste operations, and executing transfers without requiring users to understand or manage the underlying system complexity, thus improving productivity while hiding complexity from the user.
Solution Approach 2:
The system uses machine learning to detect and replicate patterns from training data where users performed copy-paste operations. By copying these learned patterns and applying them to new situations, the system achieves automated efficient operations without requiring complex manual programming, thus improving productivity with manageable complexity.
3Ease of operation
If manual navigation through multiple GUIs is performed, then adaptability to different interfaces is maintained, but task completion time increases
Solution Approach 1:
The machine learning model performs preliminary analysis of GUI fields and pre-determines the optimal copy-paste operations before execution. By analyzing the current state of multiple GUIs in advance and predicting the necessary data transfer operations, the system eliminates time-consuming manual navigation while maintaining ease of operation through automated decision-making.
Data Source
AI summary
Methods and systems for automating copy-paste operations are provided. The methods and systems generate for display a first graphical user interface (GUI) comprising one or more fields and receive user input associated with the first GUI. The methods and systems, in response to receiving the user input, analyze the one or more fields of the first GUI using a machine learning model to generate one or more copy-paste operations. The methods and systems, in response to receiving the user input, present a prompt on the first GUI comprising the one or more copy-paste operations and automatically copy data displayed in the one or more fields to one or more fields of a database.