NLP Infrastructure Upgrade Synthesis and Deployment Automation
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Solution Overview
Problem
Upgrading infrastructure platforms is a complex and time-consuming process due to the risk of disrupting hosted applications and the difficulty in identifying inactive code among a large number of scripts, leading to prolonged testing times and potential failures.
Innovation Solution
A system utilizing natural language processing to analyze proposed upgrades, generate a trust score indicating the likelihood of success, identify inactive code, and automate the deployment process by excluding inactive code, thereby reducing the risk of disruptions and simplifying the upgrade process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If natural language processing is used to analyze proposed upgrades and generate trust scores, then upgrade reliability is improved, but system complexity increases
Solution Approach 1:
The patent introduces natural language processing as an intermediary layer between the upgrade proposal and the deployment decision. The NLP system analyzes upgrade code, generates trust scores, and provides recommendations without requiring direct human analysis of complex technical details, thus improving reliability while managing complexity through automation.
Solution Approach 2:
The patent replaces manual mechanical review processes with automated NLP-based analysis. Instead of engineers manually examining upgrade code and assessing risks, the system uses natural language processing to automatically analyze, score, and evaluate upgrade proposals, substituting human mechanical review with computational analysis.
2Loss of time
If inactive code is identified and excluded from upgrades, then deployment time is reduced, but measurement precision of code status is worsened
Solution Approach 1:
The patent applies partial action by identifying and processing only the inactive portion of code for exclusion from upgrades, rather than analyzing every single line of code in detail. This approach reduces deployment time by focusing computational resources on identifying inactive code patterns while accepting that not every edge case of code status detection will be perfect.
Solution Approach 2:
The system changes the parameter of code analysis from exhaustive detailed examination to pattern-based identification. By using NLP to recognize patterns indicative of inactive code (such as commented-out sections, unused functions, or deprecated APIs), the system achieves faster identification with acceptable precision for deployment purposes.
3Productivity
If build automation scripts are generated and executed, then productivity is improved, but difficulty of detecting and measuring issues is worsened
Solution Approach 1:
The patent incorporates feedback mechanisms where the NLP system continuously monitors build automation script execution, captures results, and uses this information to refine future analysis. The system learns from deployment outcomes to improve its trust scoring and code identification accuracy over time, making the automation increasingly reliable while maintaining high productivity.
Data Source
AI summary
Embodiments of the invention are directed to a system, method, or computer program product structured for synthesizing and building infrastructure platforms. In some embodiments, the system is structured for performing a natural language synthesis of a proposed upgrade to existing infrastructure platform(s), where the natural language synthesis includes analyzing, using natural language processing, code of the proposed upgrade; generating a trust score indicating a predicted likelihood of success from results of the natural language synthesis; in response to the trust score being above a threshold, identifying, using natural language processing, inactive code in the platform(s); generating a build automation script for deploying the proposed upgrade to create upgraded infrastructure platform(s) that exclude the inactive code; executing the build automation script; capturing data from the build automation script execution; and using the result of the build automation script execution and the captured data to refine the natural language synthesis.