IT Solution Template Analytics for Cloud Resource Prediction
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Solution Overview
Problem
Source companies face challenges in accurately predicting resource capacity due to limited awareness of secondary party components' impact on their computing resources, as they primarily focus on their own components' requests from solution providers' templates.
Innovation Solution
A computer-implemented method utilizing natural language processing and object detection to analyze solution templates from solution providers, identifying source company and secondary party components, and generating analytics to reallocate cloud computing resources based on trends and future demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If source companies focus only on their own components' requests from solution providers' templates, then their component allocation is simplified and manageable, but their prediction of total resource capacity becomes inaccurate due to limited awareness of secondary party components' impact
Solution Approach 1:
The system segments solution templates into distinct components: primary components (from source company) and secondary components (from other parties). This segmentation allows the system to separately track and analyze each type of component while maintaining overall resource capacity predictions. The segmentation is achieved through natural language processing that identifies and categorizes different components within solution templates.
Solution Approach 2:
The patent introduces an intermediary analytics system that processes solution templates and extracts component information. This intermediary layer analyzes the text of solution templates to identify primary and secondary components, their relationships, and resource requirements. The intermediary translates unstructured template data into structured resource allocation data, enabling accurate predictions without requiring direct integration with all secondary parties.
2Loss of information
If source companies analyze all solution templates including secondary party components, then their resource capacity prediction improves, but the complexity of analyzing and processing template data increases
Solution Approach 1:
The patent replaces manual or simple automated analysis of solution templates with advanced natural language processing (NLP) technology. The NLP system automatically parses template text, identifies component mentions, extracts relationships between components, and determines resource requirements. This substitution of mechanical/simpler analysis methods with intelligent NLP systems enables comprehensive information extraction while reducing the perceived difficulty through automation.
Solution Approach 2:
The analytics system performs self-service by automatically processing solution templates without requiring manual intervention or complex configuration. The system autonomously identifies primary and secondary components, extracts their relationships, and generates resource capacity predictions. This self-service capability reduces the operational difficulty of analyzing templates while maintaining complete information awareness.
3Productivity
If source companies reallocate cloud computing resources based on comprehensive analytics of solution templates, then their ability to meet future demands improves, but the time and computational resources required for analysis increase
Solution Approach 1:
The system performs preliminary analysis of solution templates to predict future resource demands before actual allocation decisions are required. By analyzing templates in advance and identifying trends in primary and secondary component usage, the system prepares resource allocation recommendations ahead of time. This preliminary action enables faster response to actual allocation needs while maintaining high productivity in meeting future demands.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors resource usage patterns from analyzed solution templates and adjusts predictions and allocations accordingly. The feedback loop compares predicted versus actual resource consumption, refines the analytics model, and improves future predictions. This feedback-driven approach increases productivity over time while optimizing the time investment in analysis by focusing computational resources on the most impactful factors.
Data Source
AI summary
Aspects of the invention include identifying each solution component of a plurality of solution components described in a text of a solution template of a plurality of solution templates, wherein the solution template includes a first combination of solution components. Identifying each solution component of a plurality of solution component described by an object in the solution template of a plurality of solution templates. Detecting a respective number of instances of each solution component in the solution template and a respective number of instances of each solution component in each other solution template of the plurality of solution templates. Generating analytics for a source company based on the respective number of instances of each solution component in the solution template and the respective number of instances of each solution component in each other solution template of the plurality of solution templates.