Adaptive RPA Data Aggregation via ML Querying
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Solution Overview
Problem
Current robotic process automation (RPA) systems lack robustness and resilience, failing when encountering deviations from programmed circumstances and context, and are unable to handle data manipulation and retrieval nuances without extensive programming, leading to errors and inefficiencies.
Innovation Solution
A system utilizing machine learning to determine the type of data needed for aggregation tasks, querying multiple data sources, aligning and presenting data for user validation, and ensuring data consistency, thereby enhancing the adaptability and accuracy of RPA systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robotic agents are programmed to retrieve specific data from user interfaces with exact specifications, then data retrieval accuracy is improved, but system flexibility and adaptability deteriorate when user interfaces change or deviate from expected formats
Solution Approach 1:
The robotic agent performs self-correction by detecting errors in data retrieval or execution and automatically attempting alternative approaches without human intervention. The system monitors its own performance, identifies failures, and autonomously adjusts its behavior to overcome interface variations or data anomalies, enabling it to maintain accuracy across changing user interface formats.
2Reliability
If extensive programming is implemented to cover all possible permutations of user interface variations, then system reliability is improved, but development time and resources deteriorate
Solution Approach 1:
The robotic agent transitions from static, pre-programmed behavior to dynamic, adaptive behavior. Instead of relying on exhaustive programming for every possible scenario, the system continuously learns from executed tasks, adapts to new user interface variations encountered in real-time, and updates its data retrieval strategies dynamically, thereby achieving reliability without extensive upfront programming.
3Productivity
If robotic agents blindly process data without intelligence, then processing speed is improved, but error detection and data quality deteriorate
Solution Approach 1:
The robotic agent incorporates feedback mechanisms that allow it to evaluate the quality and validity of processed data. After retrieving or processing data, the system checks for anomalies, validates data formats, and compares results against expected patterns. This feedback loop enables the agent to detect errors while maintaining processing speed, as the validation occurs automatically during the workflow rather than requiring separate manual review steps.
Data Source
AI summary
Systems and method for use in assisting a user in data aggregation tasks. A system determines the type of data needed by the user to complete the data aggregation task and, based on an indication of the data needed, queries multiple data sources. The results from the multiple data sources are then collated and aligned as necessary. Inconsistencies in the data are resolved or flagged to the user for attention. A completed form or a presentation set of data is then presented to the user for validation.