AI Model Driven Development Framework with Action Execution
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Solution Overview
Problem
Existing model driven development frameworks lack capabilities for aggregation and execution of actions, storage and reuse of aggregated actions, and the use of artificial intelligence to interact with users, such as query/response databases and model templates to recommend actions based on user requirements.
Innovation Solution
A processor-implemented method and system that utilizes artificial intelligence to generate a model driven development framework, including an action interpreter module to identify actions and targets, an action executor to execute actions, an error identification module to locate errors, an error recovery module to rectify issues, and a recommendation engine to suggest actions based on previous executions and user inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If model driven development framework is implemented, then code generation from models is improved, but lack of aggregation and execution capabilities reduces productivity
Solution Approach 1:
The patent combines multiple separate capabilities (model creation, action definition, execution engine, pattern storage) into an integrated model-driven development framework. The execution engine aggregates actions from multiple models and executes them coordinateally, merging previously separate development tasks into a unified process that improves overall productivity while maintaining code generation capabilities.
Solution Approach 2:
The framework implements a universal execution engine that can handle multiple types of actions across different models and technologies. The system provides multi-functional capabilities including creating models, defining actions, executing actions, storing patterns, and generating code, all within a single platform that adapts to various development needs.
2Ease of manufacture
If traditional model driven framework is used, then technology specific code generation is achieved, but lack of AI interaction capabilities limits user experience
Solution Approach 1:
The patent implements feedback mechanisms where the execution engine monitors action executions, captures usage patterns, and stores them in a pattern storage component. The system learns from user interactions and provides intelligent recommendations, creating a feedback loop that continuously improves the user experience while maintaining code generation functionality.
Solution Approach 2:
The framework implements self-service capabilities through automated execution of actions based on defined patterns. The system automatically executes actions, recovers from errors using stored patterns, and provides recommendations without requiring manual intervention for each task, enabling the system to serve itself while improving user interaction.
3Productivity
If model driven framework executes actions, then functionality is implemented, but error handling and recovery capabilities are insufficient
Solution Approach 1:
The patent implements error recovery mechanisms by pre-defining recovery patterns and storing them in the pattern storage component. Before errors occur, the system prepares recovery strategies that can be automatically applied when errors are detected during action execution, ensuring reliable operation without interrupting productivity.
Solution Approach 2:
The execution engine incorporates feedback mechanisms that monitor action executions, detect errors, and trigger appropriate recovery actions based on stored patterns. The system learns from error occurrences and adjusts its behavior, providing continuous improvement in reliability while maintaining high execution productivity.
Data Source
AI summary
The system and method of the present disclosure uses the artificial intelligence in the model driven framework to bring productivity benefits in the model driven application development. It comprises an easy to use user interface to share context-specific questions to a user and to capture their responses. The system performs its user interactions based on the user's responses and the output from the recommendation module which are based on application models, user interaction history and the system usage pattern. The system interprets user inputs as one or more actions to be executed with the help of usage patterns in the model database and executes them and performs error identification and recovery when required. The results of execution or error recovery along with recommendations for subsequent actions are communicated back to the user through recommendation module.