Infrastructure Automation Platform for Task Correlation
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Solution Overview
Problem
Current techniques for performing tasks associated with systems, networks, and software require excessive manpower and waste computing and networking resources due to repetitive and mundane tasks, often resulting in incorrect or poorly performed actions.
Innovation Solution
An infrastructure automation platform utilizing natural language processing and machine learning to assist in performing actions by receiving user data, processing it to identify tasks and actions, and determining correlations to automate appropriate responses, thereby conserving resources and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual workforce is used to perform tasks on systems, networks, and software, then task completion can be achieved, but excessive manpower is required and computing resources are wasted
Solution Approach 1:
The system enables self-service automation where the infrastructure automation platform learns from user activities and automatically performs tasks without requiring continuous human intervention. The machine learning models identify patterns in user behavior and autonomously execute corresponding actions, allowing the system to serve itself by reducing dependency on manual workforce.
Solution Approach 2:
The patent replaces the mechanical human workforce with an automated infrastructure platform equipped with machine learning models. Instead of human operators manually performing tasks, the system uses trained ML models to detect user activities and automatically execute appropriate actions, substituting human mechanical operations with automated computational processes.
2Productivity
If repetitive manual tasks are performed, then task completion is achieved, but computing and networking resources are wasted
Solution Approach 1:
The system implements continuous monitoring of user activities through event streams, maintaining a persistent learning process where the machine learning models continuously analyze user behavior patterns and update their understanding. This continuous useful action ensures that automation improvements are constantly being made without interruption, maximizing resource efficiency over time.
Solution Approach 2:
The system creates copies of user activities by capturing event streams and storing them in data lakes. These copied activity records are then analyzed by machine learning models to identify patterns and automate future actions. By copying and analyzing past behaviors rather than repeating manual execution, the system learns optimal patterns and eliminates redundant resource consumption.
3Ease of operation
If manual task execution is used, then actions can be performed, but incorrect or poorly performed actions occur due to human subjectivity
Solution Approach 1:
The system implements feedback loops where user activities are continuously monitored, analyzed, and fed back into the machine learning models. The models learn from this feedback to improve their understanding of correct actions and refine their automation decisions. This continuous feedback mechanism ensures that actions become increasingly accurate over time by eliminating human subjectivity and relying on data-driven patterns.
4Measurement precision
If automation platform processes user data through natural language processing and machine learning, then task automation accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing user activities and storing them in data lakes for later analysis. Event streams are captured and prepared in advance, allowing the machine learning models to work with pre-organized data rather than processing raw data in real-time. This preliminary preparation reduces the computational burden during actual task execution and minimizes processing delays.
Data Source
AI summary
A device may receive user personalized data and user activity data identifying tasks and actions performed by a user, and may perform natural language processing on the user personalized data and the user activity data to generate processed textual data. The device may train machine learning models based on the processed textual data to generate trained machine learning models, and may receive, from a client device, a command identifying a particular task to be performed. The device may process the command and the user activity data, with the trained machine learning models, to determine whether a particular action in the user activity data correlates with the particular task. The device may perform actions when the particular action correlates with the particular task.