View source for 18587613. SYSTEMS AND METHODS FOR A MACHINE LEARNING FRAMEWORK simplified abstract (Walmart Apollo, LLC)

=SYSTEMS AND METHODS FOR A MACHINE LEARNING FRAMEWORK=


==Organization Name==

[[:Category:Walmart Apollo, LLC|Walmart Apollo, LLC]]


[[Category:Walmart Apollo, LLC]]

==Inventor(s)==

[[:Category:Vahid Jalalibarsari of Sunnyvale CA (US)|Vahid Jalalibarsari of Sunnyvale CA (US)]][[Category:Vahid Jalalibarsari of Sunnyvale CA (US)]]

==SYSTEMS AND METHODS FOR A MACHINE LEARNING FRAMEWORK - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18587613 titled 'SYSTEMS AND METHODS FOR A MACHINE LEARNING FRAMEWORK

The patent application describes a system that utilizes machine learning algorithms to process feature data and historical transactions.

* Receiving a configuration file over a computer network to identify feature data for machine learning.
* Identifying feature data using the configuration file.
* Storing feature data and historical transactions in an output file on storage devices.
* Transmitting the output file over the network for use in a machine learning application.
* Generating a machine learning algorithm for a model based on parameters in the output file.

Potential Applications:
- Financial forecasting
- Fraud detection
- Customer behavior analysis

Problems Solved:
- Efficient processing of large datasets
- Automation of machine learning model generation

Benefits:
- Improved accuracy in predictions
- Time and cost savings in data analysis

Commercial Applications:
Title: "Enhanced Machine Learning System for Data Analysis"
This technology can be used in various industries such as finance, e-commerce, and healthcare for predictive analytics and decision-making processes.

Questions about the technology:
1. How does this system improve the efficiency of machine learning model generation?
2. What are the key advantages of using historical transactions in the machine learning process?


==Original Abstract Submitted==

A system including one or more processors; and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: receiving, over a computer network, a configuration file to identify feature data for use in a machine learning algorithm; identifying, using the configuration file, the feature data; storing, in an output file on one or more storage devices, the feature data and at least a subset of historical transactions; transmitting, from the one or more storage devices and over the computer network, the output file comprising the feature data for use in a machine learning application; and generating the machine learning algorithm for a machine learning model based on parameters in the output file, wherein the output file is configured to be transferred between at least two machine learning models. Other embodiments are described.



[[Category:G06F16/25]]
[[Category:G06F9/445]]
[[Category:G06F16/2455]]
[[Category:G06N20/00]]
[[Category:H04L67/00]]
[[Category:CPC_G06F16/254]]