Self-Healing Bot for Autonomous Sub-Task Learning
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Solution Overview
Problem
Conventional automation tools struggle to identify repetitive sub-tasks within higher order, cognitive and complex tasks and determine when to autonomously act without explicit user instructions.
Innovation Solution
A self-healing bot utilizes machine learning algorithms to detect and monitor user activity, build customized libraries of tasks and sub-tasks, and autonomously execute or provide guidance on sub-tasks based on operational rules and user patterns, integrating with AI engines to enhance human-computer interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional automation tools are programmed to understand and respond to natural language requests, then user interaction capability is improved, but the ability to autonomously identify and execute repetitive sub-tasks remains insufficient
Solution Approach 1:
The system continuously monitors user interactions and task execution outcomes, using this feedback to learn and identify repetitive sub-tasks. The AI engine analyzes patterns in user behavior and system responses to autonomously determine which sub-tasks can be automated, bridging the gap between improved user interaction and autonomous execution capability
Solution Approach 2:
The automation tool performs self-learning by monitoring its own execution patterns and user interactions. It autonomously identifies repetitive sub-tasks without requiring explicit programming, allowing the system to improve its automation capabilities independently while maintaining enhanced user interaction through natural language processing
2Adaptability or versatility
If automation tools are designed to handle complex cognitive tasks, then task capability is improved, but the difficulty of isolating repetitive sub-tasks for autonomous execution increases
Solution Approach 1:
The system automatically segments complex cognitive tasks into smaller sub-tasks by monitoring user interactions and analyzing task execution patterns. The AI engine identifies repetitive components within complex tasks and isolates them for autonomous execution, reducing the complexity of sub-task management while maintaining the ability to handle sophisticated workflows
Solution Approach 2:
The AI engine acts as an intermediary between complex task execution and autonomous sub-task identification. It analyzes the relationship between user actions and system responses, mediating the complexity by translating observed patterns into identifiable repetitive sub-tasks that can be autonomously executed without requiring direct user specification
3Measurement precision
If automation tools require explicit user instructions for each action, then control precision is maintained, but productivity and efficiency are reduced
Solution Approach 1:
The system performs preliminary learning by monitoring user interactions and identifying repetitive sub-tasks before autonomous execution begins. By pre-analyzing task patterns and storing them in a library of identifiable sub-tasks, the system can later execute these sub-tasks autonomously without requiring explicit user instructions for each action, thereby improving productivity while maintaining control through the pre-established action library
Data Source
AI summary
Apparatus and methods are provided for an autonomous bot that learns from user activity. The bot then autonomously acts to complete tasks that the user typically performs. The bot may detect and act in response to detecting a problem with an application. The bot may dynamically generate guidance that assists the user completing a target task. Illustrative guidance may include highlighting differences between software applications, identifying information missing from a document or form and demonstrating how to obtain the needed information or complete a task.