View source for 18078380. GENERATING AND PROCESSING DIGITAL ASSET INFORMATION CHAINS USING MACHINE LEARNING TECHNIQUES simplified abstract (Dell Products, L.P.)

=GENERATING AND PROCESSING DIGITAL ASSET INFORMATION CHAINS USING MACHINE LEARNING TECHNIQUES=


==Organization Name==

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


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

==Inventor(s)==

[[:Category:David J. Linsey of Marietta GA (US)|David J. Linsey of Marietta GA (US)]][[Category:David J. Linsey of Marietta GA (US)]]

[[:Category:Bijan Kumar Mohanty of Austin TX (US)|Bijan Kumar Mohanty of Austin TX (US)]][[Category:Bijan Kumar Mohanty of Austin TX (US)]]

[[:Category:Hung T. Dinh of Austin TX (US)|Hung T. Dinh of Austin TX (US)]][[Category:Hung T. Dinh of Austin TX (US)]]

==GENERATING AND PROCESSING DIGITAL ASSET INFORMATION CHAINS USING MACHINE LEARNING TECHNIQUES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18078380 titled 'GENERATING AND PROCESSING DIGITAL ASSET INFORMATION CHAINS USING MACHINE LEARNING TECHNIQUES

'''Simplified Explanation:'''
This patent application describes methods, apparatus, and storage media for generating and processing digital asset information chains using machine learning techniques. It involves obtaining data related to events involving a digital asset, creating a digital asset information chain, performing anomaly detection using machine learning, and taking automated actions based on the information chain and anomaly detection results.

* Cryptographic functions are used to process data related to digital assets.
* Data is linked based on temporal parameters to create a digital asset information chain.
* Machine learning techniques are employed for anomaly detection within the information chain.
* Automated actions are triggered based on the information chain and anomaly detection results.

'''Potential Applications:'''
This technology can be applied in various industries such as finance, supply chain management, and cybersecurity to track and analyze digital asset transactions for security and compliance purposes.

'''Problems Solved:'''
This technology addresses the challenges of monitoring and analyzing digital asset transactions efficiently and accurately, especially in complex and fast-paced environments.

'''Benefits:'''
The benefits of this technology include improved security, enhanced compliance monitoring, and streamlined digital asset transaction analysis, leading to better decision-making and risk management.

'''Commercial Applications:'''
Potential commercial applications of this technology include financial institutions, e-commerce platforms, and blockchain companies looking to enhance their transaction monitoring and security measures.

'''Prior Art:'''
Prior research in the field of blockchain technology and machine learning applications in digital asset management could provide valuable insights into similar approaches.

'''Frequently Updated Research:'''
Stay updated on advancements in machine learning algorithms for anomaly detection and cryptographic techniques for secure data processing in digital asset management.

'''Questions about Digital Asset Information Chains:'''
1. How does this technology improve the security of digital asset transactions?
2. What are the key differences between traditional transaction monitoring systems and the digital asset information chain approach?


==Original Abstract Submitted==

Methods, apparatus, and processor-readable storage media for generating and processing digital asset information chains using machine learning techniques are provided herein. An example computer-implemented method includes obtaining data, from one or more data sources, pertaining to one or more events involving a digital asset; generating a digital asset information chain associated with the digital asset by processing at least a portion of the obtained data using at least one cryptographic function and linking that at least a portion of the obtained data in accordance with at least one temporal parameter; performing anomaly detection by processing at least a portion of the digital asset information chain associated with the digital asset using one or more machine learning techniques; and performing one or more automated actions based at least in part on one or more of the digital asset information chain and results from the anomaly detection.



[[Category:G06F16/215]]
[[Category:G06F16/23]]
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[[Category:CPC_G06F16/215]]