Command Engine for Free-Form Natural Language Processing
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Solution Overview
Problem
Conventional management software requires rigid and hardcoded syntax for user commands, making it difficult and expensive to change, and users need significant domain knowledge to manage and query multiple system elements efficiently, especially in complex computing systems.
Innovation Solution
A command engine that receives free-form information, identifies unsupported portions, and converts it into compatible commands using a grammar module registry, allowing for dynamic syntax and grammar processing to interact with data sets, thereby reducing the need for extensive domain knowledge and improving user productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rigid and hardcoded syntax is used for user commands, then command processing reliability is improved, but adaptability and ease of operation deteriorate
Solution Approach 1:
The patent applies dynamics by transforming the static, hardcoded syntax into a dynamic natural language processing system. The command engine dynamically adapts to different user inputs by using machine learning models and natural language understanding, allowing the system to evolve and accommodate new command patterns without requiring changes to the underlying rigid structure.
Solution Approach 2:
The patent introduces an intermediary layer (natural language processing module) between the user and the command execution system. This intermediary translates free-form natural language inputs into structured commands that the rigid backend system can process, thereby maintaining reliability while improving adaptability and ease of use.
2Stability of the object's composition
If rigid and hardcoded syntax is used for user commands, then system stability is improved, but ease of operation and user productivity deteriorate
Solution Approach 1:
The natural language processing intermediary allows users to interact with the stable, rigid system using flexible, natural language. This mediator handles the complexity of translation, maintaining system stability while dramatically improving ease of operation.
Solution Approach 2:
The system provides self-service by automatically understanding and interpreting natural language commands without requiring users to learn complex syntax rules. The machine learning models continuously improve their understanding, making the system increasingly easy to use while maintaining stability.
3Adaptability or versatility
If free-form natural language commands are accepted, then adaptability and ease of operation are improved, but processing complexity increases
Solution Approach 1:
The patent segments the complex natural language processing task into multiple manageable components: intent recognition, entity extraction, command parameter identification, and execution mapping. This segmentation reduces processing complexity by handling each aspect separately while maintaining overall flexibility.
Solution Approach 2:
The natural language processing intermediary manages the complexity by providing a structured translation layer. It converts unstructured natural language into standardized command formats, thereby maintaining adaptability while controlling processing complexity through systematic transformation rules.
4Productivity
If free-form natural language commands are accepted, then user productivity is improved, but measurement precision and command interpretation accuracy may deteriorate
Solution Approach 1:
The system implements feedback mechanisms where the command engine provides suggestions, clarifications, or confirmation requests when natural language inputs are ambiguous. This feedback loop allows users to correct or refine their inputs, maintaining high productivity while ensuring accurate interpretation.
Solution Approach 2:
The patent replaces traditional mechanical syntax-matching systems with intelligent natural language understanding based on machine learning. This substitution enables the system to comprehend context and intent, improving accuracy in interpreting free-form commands while maintaining high user productivity.
Data Source
AI summary
Methods, systems, and computer readable mediums for command engine execution are disclosed. One method for command engine execution includes receiving free-form information for requesting or modifying information about a computing system. The method also includes identifying a portion in the free-form information that is unsupported by a command engine. The method further includes converting, using a grammar module that supports the portion, the free-form information into at least one compatible command for interacting with at least one data set. The method also includes requesting or modifying the information about the computing system by interacting with the at least one data set using the at least one compatible command.