18542998. DATA FLOW CONTROL AND ROUTING USING MACHINE LEARNING simplified abstract (Bank of America Corporation)
Contents
- 1 DATA FLOW CONTROL AND ROUTING USING MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DATA FLOW CONTROL AND ROUTING USING MACHINE LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
DATA FLOW CONTROL AND ROUTING USING MACHINE LEARNING
Organization Name
Inventor(s)
Rama Venkata S. Kavali of Frisco TX (US)
Venugopala Rao Randhi of Hyderabad (IN)
Damodarrao Thakkalapelli of Plano TX (US)
Vijaya Kumar Vegulla of Hyderabad (IN)
Rajasekhar Maramreddy of Hyderabad (IN)
DATA FLOW CONTROL AND ROUTING USING MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18542998 titled 'DATA FLOW CONTROL AND ROUTING USING MACHINE LEARNING
Simplified Explanation
The abstract describes a device that can identify and remove links between data elements, input them into a machine learning model, create relationship tables, generate data streams, and output the data stream.
- The device identifies and removes links between data elements.
- It inputs data elements into a machine learning model to output new links.
- It creates relationship tables to identify data element connections.
- It generates data streams with the connected data elements.
- It outputs the data stream for further use.
Potential Applications
This technology could be applied in various fields such as data analysis, information retrieval, and knowledge management systems.
Problems Solved
This technology solves the problem of efficiently managing and analyzing large sets of data elements by creating and updating links between them.
Benefits
The benefits of this technology include improved data organization, enhanced data analysis capabilities, and increased efficiency in data processing tasks.
Potential Commercial Applications
A potential commercial application of this technology could be in the development of data management software for businesses looking to streamline their data processing operations.
Possible Prior Art
One possible prior art for this technology could be existing data integration tools that help in linking and managing data elements in databases.
What is the accuracy rate of the machine learning model used in this device?
The accuracy rate of the machine learning model used in this device is not specified in the abstract.
How does this device handle data elements with missing values?
The abstract does not mention how this device handles data elements with missing values.
Original Abstract Submitted
A device configured to identify a first link between a value of a first data element in a first plurality of data elements and values of a first set of data elements in a second plurality of data elements and to remove the first link between the first data element and the first set of data elements. The device is further configured to input the data elements into a machine learning model that is configured to output a second link between the first data element and a second set of data elements. The device is further configured to create an entry in a relationship table that identifies the first data element and the second set of data elements. The device is further configured to generate a data stream with the first data element and the second set of data elements and to output the data stream.
