AI Automation Orchestration for Natural-Language Task Execution
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Solution Overview
Problem
Existing automations and robotic process automations (RPAs) are not effectively leveraged to accomplish specific user goals, leading to unused conventional automations and the need for new AI models to create new automations.
Innovation Solution
An automation engine integrates generative artificial intelligence models with existing automations and RPAs to process natural language problem statements, determining a processing strategy and executing computer-generated scripts to achieve user-defined tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional automations are used to accomplish specific user goals, then the automation capability is limited, but the system complexity increases due to creating new automations
Solution Approach 1:
The patent introduces an AI model as an intermediary layer between the user's natural language request and the conventional automation system. The AI model translates human intent into automation-compatible instructions, enabling existing automations to handle new tasks without modifying the automations themselves. This resolves the contradiction by maintaining automation versatility through the AI intermediary while preserving system simplicity.
Solution Approach 2:
The patent makes the conventional automation system universal by integrating it with an AI model that can interpret various natural language inputs. Instead of creating specialized automations for each task, the system uses a single AI-powered interface that adapts to different user goals, reducing the need for multiple specialized automations and thereby decreasing system complexity.
2Productivity
If new automations are created to accomplish specific tasks, then the task completion capability improves, but the loss of existing automation resources increases
Solution Approach 1:
The patent performs preliminary action by pre-training AI models on automation-related tasks and knowledge before deployment. The AI model is prepared in advance to understand and leverage existing automations, enabling it to efficiently accomplish new tasks by combining its pre-acquired knowledge with available automation resources rather than creating everything from scratch.
Solution Approach 2:
The patent merges the capabilities of existing conventional automations with the interpretive power of AI models. Instead of discarding or replacing existing automations, the system combines them with AI technology, allowing the merged system to accomplish new tasks while preserving and reusing the original automation resources.
3Adaptability or versatility
If AI models create new automations from scratch, then the ability to handle specific tasks improves, but the time consumption increases
Solution Approach 1:
The patent ensures continuity of useful action by designing the AI model to continuously learn from and leverage existing automation patterns. Once the AI model is trained, it can rapidly interpret new natural language requests and map them to appropriate automation actions without repeatedly creating automations from scratch, maintaining both adaptability and efficiency.
Solution Approach 2:
The patent performs preliminary training of the AI model on automation tasks and existing automation patterns before deployment. This advance preparation enables the model to quickly process new requests by applying pre-learned knowledge rather than starting from zero each time, reducing time consumption while maintaining versatile task handling capability.
Data Source
AI summary
A method is provided. The method is executed by an automation engine implemented as a computer program within a computing environment. The automation engine executing processing strategy based on a problem statement. The method includes receiving the problem statement comprising a specific requested task in natural language and automatically processing the problem statement by utilizing a generative artificial intelligence (AI) model to manipulate the natural language of the problem statement to create a new computer digestible script defining the specific requested task. The method includes automatically determining the processing strategy comprising an automation plan of computer generated steps that achieves the specific requested task and executing the plurality of existing automations according to the processing strategy.