AI GUI Action Prediction for Near-Repetitive Data Entry
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Solution Overview
Problem
Existing technologies fail to efficiently address the repetitive tasks in electronic data entry, especially for users with electronic data entry, where the user's repetitive tasks involving electronic data entry, and the user's repetitive actions, and the user's repetitive actions, such as electronic data entry, and the user's repetitive actions, especially when these activities involve limited quantities of data, especially when these activities involve limited quantities of data, especially when these activities involve limited quantities of data.
Innovation Solution
A processor system and storage system that tracks user inputs and executes a prediction model to identify near-repetitive actions, presenting foreshadow actions on the display, which can be selected by the user to command the processor to autonomously perform the corresponding action, using artificial neural networks for pattern recognition and machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually perform repetitive data entry tasks, then accuracy and adaptability to variations are maintained, but time consumption increases significantly
Solution Approach 1:
The system performs preliminary analysis of historical user inputs and GUI interactions to predict future actions before the user actually performs them. The prediction model processes past behavior patterns and pre-computes suggested actions, which are then presented to the user for confirmation, thereby reducing the time needed for repetitive data entry tasks.
Solution Approach 2:
The system enables self-service automation where the prediction model autonomously analyzes user behavior patterns and generates personalized action predictions without requiring explicit programming or configuration. The model serves itself by continuously learning from user interactions and automatically adapting to variations in tasks.
2Extent of automation
If task-specific software tools are developed to automate repetitive actions, then automation capability improves, but development and debugging time increases
Solution Approach 1:
The prediction model performs self-service by automatically learning from historical user data and generating personalized action predictions without requiring external programming or configuration. This eliminates the need for developers to create custom automation tools for each task, significantly reducing development and debugging time.
Solution Approach 2:
The system provides a universal prediction model that can handle multiple types of repetitive tasks across different GUI applications without requiring task-specific software tools. The model generalizes patterns from historical data to predict actions for various tasks, making a single system serve multiple automation needs.
3Extent of automation
If existing automation tools are used, then repetitive actions can be automated, but adaptability to variations in small-batch tasks is lost
Solution Approach 1:
The prediction model dynamically adapts to variations in tasks by continuously learning from historical user interactions and adjusting its predictions in real-time. Unlike static automation rules, the model can handle variations in small-batch tasks by analyzing patterns in the specific context of each interaction sequence, maintaining both automation and adaptability.
Solution Approach 2:
The system changes its prediction parameters based on the specific context of each task variation. By analyzing historical data with varying parameters (different GUI elements, data formats, task contexts), the model adjusts its prediction behavior to accommodate variations while maintaining automation, allowing it to adapt to small-batch task requirements.
4Productivity
If AI prediction models are implemented, then repetitive actions can be identified and automated, but system complexity increases
Solution Approach 1:
The prediction model creates a simplified representation (copy) of user behavior patterns from historical data. Instead of implementing complex rule-based automation systems, the model learns and copies the essential patterns from actual user interactions, generating predictions that replicate human decision-making processes in a computationally efficient manner.
Solution Approach 2:
The system reduces complexity through self-service learning, where the prediction model automatically extracts useful patterns from historical data without requiring manual programming of complex automation logic. The model serves itself by learning from past behavior, eliminating the need for complex external configuration or programming while achieving effective automation.
Data Source
AI summary
In one aspect, a device includes a processor system and storage accessible to the processor system. The storage includes instructions executable by the processor system to track user inputs as a user interacts with a first graphical user interface (GUI) presented on a display. The instructions are also executable to execute a prediction model to identify a near-repetitive action from the user inputs. Based on the identification of the near-repetitive action, the instructions are executable to present a foreshadow action on the display. The foreshadow action is selectable to command the processor system to autonomously perform a real action corresponding to the foreshadowed action.