Dell Products, L.P. (20240232606). COMPUTING SERVICES ARCHITECT simplified abstract
Contents
COMPUTING SERVICES ARCHITECT
Organization Name
Inventor(s)
Nisanth Mathilakath Padinharepatt of Palakkad District (IN)
Pratika Dola of Bangalore (IN)
Ashish Gupta of Bangalore (IN)
COMPUTING SERVICES ARCHITECT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240232606 titled 'COMPUTING SERVICES ARCHITECT
The abstract describes a computing system module that helps design a cloud computing services application with multiple disparate cloud computing services from various sources.
- The module converts a description of desired functionality into a context vector.
- A trained supervised learning model analyzes the context vector to determine the probability of including each available computing service.
- The model generates a recommendation report based on the probabilities, suggesting which services to include in the application.
- The model can also determine an architecture of recommended services based on the context vector.
- The architecture can be visualized as a diagram, and the model can be updated with information from actual use.
Potential Applications: - Designing complex cloud computing applications with multiple services. - Optimizing the selection of cloud computing services for specific applications.
Problems Solved: - Streamlining the process of selecting and integrating multiple cloud computing services. - Improving the efficiency and effectiveness of cloud computing application design.
Benefits: - Enhanced decision-making in selecting cloud computing services. - Increased efficiency in designing cloud computing applications. - Improved performance and functionality of cloud computing applications.
Commercial Applications: Cloud service providers, software development companies, and businesses utilizing cloud computing can benefit from this technology to optimize their applications and services.
Prior Art: Researchers and developers in the field of cloud computing, machine learning, and artificial intelligence may have explored similar approaches to optimizing cloud computing applications.
Frequently Updated Research: Stay updated on advancements in machine learning algorithms for cloud computing optimization and the integration of diverse cloud services.
Questions about Cloud Computing Services Application Design: 1. How does the computing system module determine the relative probability of including each available computing service? 2. What are the key factors considered by the trained supervised learning model when analyzing the context vector for cloud computing service recommendations?
Original Abstract Submitted
a computing system module facilitates designing a cloud computing services application that comprises multiple disparate cloud computing services available from multiple sources, vendors, or platforms. a description, in textual or verbal form, of desired functionality of the application is converted into a context vector. a trained supervised learning model having a number of nodes corresponding to a number of available computing services, analyzes the context vector and determines a relative probability for each node with respect to probability thresholds. the learning model identifies in a recommendation report that the application should include a service if a probability corresponding to the service satisfies a respective criterion. edges may be determined from the context vector and analyzed by the learning model to determine an architecture of recommended services. the architecture may be rendered as a visual diagram based on the edges. information from actual use may update training of the learning model.
