View source for 18099586. CHARACTERIZING COMPUTER INFRASTRUCTURE USING MACHINE LEARNING TECHNIQUES simplified abstract (Dell Products L.P.)

=CHARACTERIZING COMPUTER INFRASTRUCTURE USING MACHINE LEARNING TECHNIQUES=


==Organization Name==

[[:Category:Dell Products L.P.|Dell Products L.P.]]


[[Category:Dell Products L.P.]]

==Inventor(s)==

[[:Category:Deborah C. Russell of Arvada CO (US)|Deborah C. Russell of Arvada CO (US)]][[Category:Deborah C. Russell of Arvada CO (US)]]

[[:Category:Muzhar S. Khokhar of Shrewsbury MA (US)|Muzhar S. Khokhar of Shrewsbury MA (US)]][[Category:Muzhar S. Khokhar of Shrewsbury MA (US)]]

[[:Category:Claudia Realegeno of Maple Valley WA (US)|Claudia Realegeno of Maple Valley WA (US)]][[Category:Claudia Realegeno of Maple Valley WA (US)]]

[[:Category:Nikhil Manohar Vichare of Austin TX (US)|Nikhil Manohar Vichare of Austin TX (US)]][[Category:Nikhil Manohar Vichare of Austin TX (US)]]

==CHARACTERIZING COMPUTER INFRASTRUCTURE USING MACHINE LEARNING TECHNIQUES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18099586 titled 'CHARACTERIZING COMPUTER INFRASTRUCTURE USING MACHINE LEARNING TECHNIQUES

The abstract describes methods, apparatus, and processor-readable storage media for characterizing computer infrastructure using machine learning techniques. 

* Obtaining a machine learning model trained on a set of training data to generate labels for computer infrastructure elements based on user interactions and configuration information.
* Generating additional labels for computer infrastructure elements using the machine learning model and information on user interactions and configuration data.
* Performing automated actions related to the computer infrastructure elements based on the additional labels.

Potential Applications:
- Automated monitoring and management of computer infrastructure.
- Predictive maintenance of computer systems.
- Optimization of resource allocation in IT environments.

Problems Solved:
- Efficiently characterizing and managing complex computer infrastructure.
- Enhancing system performance and reliability through proactive actions.
- Streamlining IT operations and reducing manual intervention.

Benefits:
- Improved system performance and reliability.
- Cost savings through optimized resource allocation.
- Enhanced security and compliance with automated actions.

Commercial Applications:
Title: "Machine Learning for Computer Infrastructure Characterization"
This technology can be used in data centers, cloud computing environments, and enterprise IT systems to automate monitoring, maintenance, and optimization processes. It can benefit IT service providers, large corporations, and organizations with extensive computer infrastructure.

Questions about Machine Learning for Computer Infrastructure Characterization:
1. How does this technology improve the efficiency of managing computer infrastructure?
2. What are the key advantages of using machine learning for characterizing computer systems?


==Original Abstract Submitted==

Methods, apparatus, and processor-readable storage media for characterizing computer infrastructure using machine learning techniques are provided herein. An example computer-implemented method includes obtaining a machine learning model that is trained, using a set of training data, to generate one or more labels for one or more of a plurality of computer infrastructure elements, where the training data is based on information corresponding to user interactions and configuration information associated with at least a portion of the computer infrastructure elements; generating, using the machine learning model, at least one additional label for at least one additional computer infrastructure element using information corresponding to one or more user interactions and configuration information associated with the at least one additional computer infrastructure element; and performing one or more automated actions related to the at least one additional computer infrastructure element based at least in part on the at least one additional label.



[[Category:G06N3/08]]
[[Category:G06N3/044]]
[[Category:CPC_G06N3/08]]