Natural Language Rule Generation for Infrastructure Monitoring
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Solution Overview
Problem
Existing infrastructure monitoring systems face complexity in specifying rules, particularly for users unfamiliar with programming languages or forms, leading to errors and malfunctions in monitoring specific infrastructure components.
Innovation Solution
A machine learning model processes natural-language expressions to generate rules, enabling users to configure monitoring engines intuitively and accurately, accounting for specific infrastructure details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If programming languages or forms are used to specify rules, then rule specification precision is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
A natural language processing intermediary is introduced between the user and the rule specification system. This intermediary translates casual natural language inputs into precise rule definitions, eliminating the need for users to directly interact with complex programming languages or forms while maintaining high precision in rule specification.
Solution Approach 2:
The patent replaces the mechanical interaction with complex forms and programming languages by substituting it with a linguistic-based system. Natural language processing algorithms interpret user intent and automatically generate rule specifications, replacing the manual mechanical process of filling forms with an automated linguistic understanding system.
2Manufacturing precision
If programming languages or forms are used to specify rules, then rule specification precision is improved, but ease of operation deteriorates
Solution Approach 1:
The natural language processing system acts as an intermediary that bridges user intent and rule specification. Users can express their monitoring requirements in everyday language, and the system automatically translates this into precise rule definitions, making the process accessible to non-technical users while maintaining high precision.
Solution Approach 2:
The system enables users to create rules independently through natural language inputs without requiring training on complex forms or programming languages. The automated interpretation and translation capabilities allow users to service themselves in rule creation, eliminating the need for specialized knowledge or extensive form-filling processes.
3Ease of operation
If natural-language expressions are processed to generate rules, then ease of operation is improved, but measurement precision may deteriorate
Solution Approach 1:
The patent replaces manual form-filling mechanics with automated natural language processing mechanics. Advanced NLP algorithms interpret user intent, disambiguate meanings, and generate precise rule specifications automatically, ensuring that the ease of natural language input does not compromise the precision of the resulting rules.
Solution Approach 2:
The system incorporates feedback mechanisms where the processed natural language input is presented back to the user for confirmation or correction before final rule generation. This feedback loop ensures that the automated interpretation accurately reflects user intent, maintaining high precision while preserving the ease of natural language interaction.
Data Source
AI summary
In some embodiments, a method includes processing, by a machine learning model, a natural-language expression to generate one or more rules, each rule including a trigger and one or more actions; monitoring a deployed infrastructure to detect an occurrence of at least one of the one or more triggers of a first rule of the one or more rules; and performing at least one of the one or more actions of the first rule based on the occurrence of at least one of the one or more triggers.