View source for 18317006. SYSTEMS AND METHODS FOR MANUFACTURED DATA GENERATION AND MANAGEMENT VIA AN INTERACTIVE MANUFACTURED DATASET LIBRARY simplified abstract (Wells Fargo Bank, N.A.)

=SYSTEMS AND METHODS FOR MANUFACTURED DATA GENERATION AND MANAGEMENT VIA AN INTERACTIVE MANUFACTURED DATASET LIBRARY=


==Organization Name==

[[:Category:Wells Fargo Bank, N.A.|Wells Fargo Bank, N.A.]]


[[Category:Wells Fargo Bank, N.A.]]

==Inventor(s)==

[[:Category:Dale C. Miller of Winston-Salem NC (US)|Dale C. Miller of Winston-Salem NC (US)]][[Category:Dale C. Miller of Winston-Salem NC (US)]]

[[:Category:Carrie Anne Hanson of Charlotte NC (US)|Carrie Anne Hanson of Charlotte NC (US)]][[Category:Carrie Anne Hanson of Charlotte NC (US)]]

==SYSTEMS AND METHODS FOR MANUFACTURED DATA GENERATION AND MANAGEMENT VIA AN INTERACTIVE MANUFACTURED DATASET LIBRARY - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18317006 titled 'SYSTEMS AND METHODS FOR MANUFACTURED DATA GENERATION AND MANAGEMENT VIA AN INTERACTIVE MANUFACTURED DATASET LIBRARY

==Simplified Explanation==

The patent application describes a method for generating and managing manufactured datasets based on user input requirements.

* Communications hardware receives a user input set indicating data manufacture requirements.
* Query generation circuitry generates a manufactured dataset library query based on the requirements.
* Communications hardware receives a set of results containing one or more manufactured datasets from the library.
* Dataset generation circuitry generates a manufactured dataset based on the received results.

== Potential Applications ==
This technology could be applied in industries such as data analysis, machine learning, and artificial intelligence for generating synthetic datasets for training models.

== Problems Solved ==
This technology solves the problem of efficiently generating diverse datasets for various applications without the need for manual data collection and processing.

== Benefits ==
The benefits of this technology include faster dataset generation, increased data diversity, and improved model training capabilities.

== Potential Commercial Applications ==
Potential commercial applications of this technology include data science platforms, AI model training services, and research institutions looking to generate large datasets for experimentation.

== Possible Prior Art ==
One possible prior art for this technology could be the use of data augmentation techniques in machine learning to artificially increase the size of training datasets.

=== What are the potential security implications of using manufactured datasets in sensitive applications? ===

Using manufactured datasets in sensitive applications could raise concerns about data privacy and security, as the generated data may not accurately reflect real-world scenarios. Ensuring the confidentiality and integrity of manufactured datasets becomes crucial in such cases.

=== How can the quality and diversity of manufactured datasets be measured and improved over time? ===

The quality and diversity of manufactured datasets can be measured by evaluating the performance of models trained on them and comparing the results with real-world data. Continuous feedback loops and iterative improvements in the dataset generation process can help enhance the quality and diversity of manufactured datasets.


==Original Abstract Submitted==

Systems, apparatuses, methods, and computer program products are disclosed for manufactured dataset generation and management. An example method includes receiving, by communications hardware, a user input set indicating data manufacture requirements. The example method also includes generating, by query generation circuitry, a manufactured dataset library query based on the data manufacture requirements. The example method also includes receiving, by the communications hardware and based on an execution of the manufactured dataset library query, a set of results comprising one or more manufactured datasets of a manufactured dataset library, the one or more manufactured datasets having been previously generated based on one or more previously received user input sets. The example method also includes generating, by dataset generation circuitry, a manufactured dataset based on the set of results.



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