AI Engine Identifies Desktop Automation Sequences
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Solution Overview
Problem
Current methods for identifying robotic automation opportunities in business processes are manual, time-consuming, and costly, making it difficult to determine the return on investment (ROI) and leading to missed automation opportunities with potential bias.
Innovation Solution
A system and method using a machine learning model to identify and automate significant sequences of desktop events based on ROI potential, involving a database system, automation finder engine, and portal that processes user actions to split and filter repetitive sequences, building templates for dynamic text entry and business process automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to identify robotic automation opportunities, then human judgment and expertise can be applied, but the process becomes time-consuming, costly, and prone to bias
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated machine learning system that uses computer vision and natural language processing to analyze screen recordings and identify automation opportunities. The system processes video feeds from screen recording software, automatically detects user interactions, and generates ROI assessments without human intervention, thereby eliminating time loss and bias while maintaining or improving accuracy.
Solution Approach 2:
The system enables self-service automation opportunity identification by allowing the software to autonomously analyze its own usage patterns and generate automation recommendations. The machine learning model continuously learns from user interactions and automatically identifies processes suitable for automation, eliminating the need for external manual analysis and enabling the system to serve itself in identifying optimization opportunities.
2Reliability
If manual analysis is used to determine ROI for automation opportunities, then detailed human evaluation can be performed, but the process becomes costly and misses significant automation opportunities
Solution Approach 1:
The patent creates a universal system that performs multiple functions: screen recording, event detection, sequence identification, ROI calculation, and automation recommendation generation. This multi-functional platform can analyze any desktop application uniformly, ensuring comprehensive identification of automation opportunities across the entire software suite without requiring separate manual analysis processes for different applications.
Solution Approach 2:
The system introduces an intermediary machine learning layer between raw screen recordings and final automation recommendations. This intermediary processes the video feed, extracts meaningful events, identifies patterns, and generates structured ROI assessments, thereby simplifying the overall process while improving reliability by systematically analyzing all potential opportunities rather than relying on selective manual review.
3Productivity
If users repeatedly perform manual inputs, then flexibility and adaptability are maintained, but processing time increases and user experience deteriorates
Solution Approach 1:
The system performs preliminary actions by recording and analyzing user interactions during normal usage, building a library of automation opportunities before actual automation is implemented. The machine learning model continuously learns from recorded events and pre-identifies processes that would benefit from automation, preparing recommendations in advance so that when automation is deployed, it immediately improves productivity without requiring users to adapt their workflows.
Solution Approach 2:
The system creates digital copies of user interactions by recording screen events and reconstructing them as structured data sequences. These copies are then analyzed by the machine learning model to identify repetitive patterns suitable for automation. By working with copies rather than requiring users to manually document processes, the system maintains ease of operation while enabling automated analysis that improves processing speed.
Data Source
AI summary
A system is provided for an artificial intelligence engine adapted to identify robotic process automation' opportunities based on return on investment (ROI) potential for automation. The system includes a processor and a computer readable medium configured to perform operations comprising receiving an event log of a plurality of user actions, splitting the plurality of user actions into a plurality of user action sentences, determining a sequence of user actions in the plurality of user action sentences based on a recurrence for the sequence in the plurality of user action sentences, determining a score for the sequence based on a time duration in which the user completes the sequence and based on types of the plurality of user actions in the sequence, and filtering the sequence with a plurality of other sequences.