Adaptive Task Automation via Semantic Ontology and Intent Recognition
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Solution Overview
Problem
Existing automation technologies, such as macros, are limited in their ability to adapt to changes in software applications and websites, as they rely on specific sequences of user actions and lack understanding of the processes they perform, making them ineffective across different applications and prone to becoming obsolete with updates.
Innovation Solution
A system that allows users to teach computational devices to perform repetitive tasks using natural language and demonstrations, utilizing a language understanding module, intent recognition component, and semantic ontology to interpret user intent and adapt to changes in web environments, enabling flexible and adaptive task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If macros are used to automate tasks, then task execution speed is improved, but adaptability to application changes deteriorates
Solution Approach 1:
The system continuously monitors the application environment and compares current state with expected state, providing feedback loops that enable automatic adaptation when changes are detected, thus maintaining both execution speed and adaptability
Solution Approach 2:
The macro system transitions from static, pre-defined sequences to dynamic, adaptive execution where the automation logic can adjust to environmental changes in real-time, resolving the contradiction between speed and adaptability
2Manufacturing precision
If specific sequences of user actions are recorded for automation, then task precision is improved, but versatility across different applications deteriorates
Solution Approach 1:
The system creates universal automation logic that can operate across multiple applications and contexts, rather than application-specific macros, enabling the same automation framework to achieve precise tasks in diverse environments
3Reliability
If detailed action sequences are predefined, then task reliability is improved, but ease of modification deteriorates
Solution Approach 1:
The automation system becomes dynamic and self-adjusting, reducing the need for manual modifications while maintaining reliability through continuous environmental monitoring and automatic adaptation
Data Source
AI summary
A system which allows a user to teach a computational device how to perform complex, repetitive tasks that the user usually would perform using the device's graphical user interface (GUI) often but not limited to being a web browser. The system includes software running on a user's computational device. The user “teaches” task steps by inputting natural language and demonstrating actions with the GUI. The system uses a semantic ontology and natural language processing to create an explicit representation of the task that is stored on the computer. After a complete task has been taught, the system is able to automatically execute the task in new situations. Because the task is represented in terms of the ontology and user's intentions, the system is able to adapt to changes in the computer code while still pursuing the objectives taught by the user.