View source for 17934299. META-LEARNING OPERATION RESEARCH OPTIMIZATION simplified abstract (Oracle International Corporation)

=META-LEARNING OPERATION RESEARCH OPTIMIZATION=


==Organization Name==

[[:Category:Oracle International Corporation|Oracle International Corporation]]


[[Category:Oracle International Corporation]]

==Inventor(s)==

[[:Category:Amir Hossein Rezaeian of San Mateo CA (US)|Amir Hossein Rezaeian of San Mateo CA (US)]][[Category:Amir Hossein Rezaeian of San Mateo CA (US)]]

[[:Category:Hariharan Balasubramanian of Redmond WA (US)|Hariharan Balasubramanian of Redmond WA (US)]][[Category:Hariharan Balasubramanian of Redmond WA (US)]]

==META-LEARNING OPERATION RESEARCH OPTIMIZATION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17934299 titled 'META-LEARNING OPERATION RESEARCH OPTIMIZATION

==Simplified Explanation==

The present disclosure describes systems and methods for optimizing service through operation research using machine learning models.

* Data processing system receives a payload with a request for service optimization.
* Meta learning classifier processes the payload by extracting problem and use case characteristics.
* At least one machine learning model is predicted to solve the problem based on the characteristics.
* The predicted model is executed to provide a solution for optimizing the service.
* The solution is outputted and provided to a computing device.

== Potential Applications ==
This technology can be applied in various industries such as logistics, supply chain management, healthcare, finance, and manufacturing to optimize operations and improve efficiency.

== Problems Solved ==
1. Streamlining service optimization processes.
2. Enhancing decision-making based on data-driven insights.

== Benefits ==
1. Increased efficiency and productivity.
2. Cost savings through optimized operations.
3. Improved service quality and customer satisfaction.

== Potential Commercial Applications ==
Optimization software for businesses in various sectors.
SEO Optimized Title: "Revolutionizing Service Optimization with Machine Learning"

== Possible Prior Art ==
One possible prior art could be traditional operation research methods that do not utilize machine learning models for service optimization.

== Unanswered Questions ==
=== How does the meta learning classifier determine the appropriate machine learning model for a specific problem? ===
The abstract does not provide details on the specific criteria or algorithms used by the meta learning classifier to predict the machine learning model.

=== What types of services can be optimized using this technology? ===
The abstract mentions optimizing a service, but it does not specify the range or types of services that can benefit from this system and method.


==Original Abstract Submitted==

The present disclosure generally relates to systems and methods for operation research optimization. The systems and methods include receiving, at a data processing system, a payload including a request for optimizing a service and processing the payload using a meta learning classifier. The processing includes extracting a problem and use case characteristics from the payload, predicting at least one machine learning model capable of solving the problem having the use case characteristics, and executing the at least one machine learning model to solve the problem. The systems and methods also include outputting a solution to the problem for optimizing the service from the at least one machine learning model, and providing the solution to a computing device.



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