18078380. GENERATING AND PROCESSING DIGITAL ASSET INFORMATION CHAINS USING MACHINE LEARNING TECHNIQUES simplified abstract (Dell Products, L.P.)
Contents
GENERATING AND PROCESSING DIGITAL ASSET INFORMATION CHAINS USING MACHINE LEARNING TECHNIQUES
Organization Name
Inventor(s)
David J. Linsey of Marietta GA (US)
Bijan Kumar Mohanty of Austin TX (US)
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.
