Cloud Implementation Strategy Generation Using NLP and R-Lane Models
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Solution Overview
Problem
Existing methods for cloud implementation of IT infrastructure are limited in capturing requirements and data for migration strategies, lack AI-based natural language processing, and require significant manual work, failing to provide a systematic and automated approach.
Innovation Solution
A method and system that utilize natural language processing to receive inputs from stakeholders and machine-acquired data points, label them, map patterns to cloud implementation R-lane models, classify problem domains, and determine implementable solutions using search, backtracking, or constraint propagation models, generating an output artifact based on a belief-desire-intention model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing data center discovery capabilities are used to discover entire data center infrastructure, then data collection coverage is improved, but the ability to apply semantic meaning or reasoning to business and operational criticality deteriorates
Solution Approach 1:
The patent segments the data collection and processing into two distinct components: machine-driven discovery tools that collect infrastructure data at scale, and human experts who provide semantic labeling and reasoning about business criticality. This segmentation allows each component to excel at its specialized function while avoiding the limitations of trying to do both automatically.
Solution Approach 2:
The patent introduces human experts as an intermediary layer between machine-collected data and cloud migration strategy formulation. These humans act as mediators who translate raw infrastructure data into semantically meaningful information about business criticality, application dependencies, and operational requirements that machines cannot independently determine.
2Ease of manufacture
If static forms are used to capture business criticality, then implementation simplicity is improved, but the quality and intelligence of requirement capture deteriorates
Solution Approach 1:
The patent replaces traditional mechanical/static forms with an intelligent system that combines machine learning algorithms and natural language processing. This substitution enables the system to dynamically understand and capture business criticality information with much higher quality, while still maintaining ease of use through automated interactions.
3Measurement precision
If manual work is used to collect and collate information for cloud implementation, then information accuracy is improved, but productivity and automation level deteriorates
Solution Approach 1:
The patent merges the strengths of manual expert analysis with automated machine processing. Human experts provide accurate semantic labeling and reasoning, while machine-driven tools handle large-scale data collection, pattern recognition, and strategy generation. This combination achieves both high information accuracy and high productivity simultaneously.
Solution Approach 2:
The patent uses machine learning models to learn from expert-labeled data and automatically generate cloud migration strategies. The system creates computational copies of expert reasoning capabilities that can be applied consistently across multiple data centers and infrastructure assessments, scaling accuracy to industrial levels of productivity.
4Adaptability or versatility
If existing solutions are used to design and optimize cloud implementation models, then manual expertise utilization is improved, but automation and systematic information provision deteriorates
Solution Approach 1:
The patent performs preliminary actions by having human experts label and annotate infrastructure data with semantic meaning about business criticality before the automated analysis begins. This preparatory human input enables the subsequent automated machine learning models to generate accurate cloud migration strategies without requiring manual intervention at each step, achieving both expertise utilization and automation.
Data Source
AI summary
This disclosure relates to method and system for generating strategy and roadmap for end-to-end information technology (IT) infrastructure cloud implementation. The method may include receiving natural language inputs from stakeholders with respect to a current IT infrastructure and a future IT infrastructure cloud implementation, receiving machine acquired data points from data centers, labelling human semantic data points in the natural language inputs and the machine acquired data points into labelled data points, mapping patterns in the labelled data points to a cloud implementation R-lane models, classifying a problem domain into classes of a cloud strategy based on at least one of the cloud implementation R-lane models, determining an implementable solution using at least one of a search, a backtracking, or a constraint propagation model to business constrains that are identified based on the classes, and generating an output artifact based on the implementable solution using a belief-desire-intention model.