Stillmark Management LLC (20240378573). GEOMETRIC DEEP LEARNING AND OPTIMIZATION OF PAYMENTS ON A SECOND-LAYER PAYMENT PROTOCOL NETWORK simplified abstract
Contents
GEOMETRIC DEEP LEARNING AND OPTIMIZATION OF PAYMENTS ON A SECOND-LAYER PAYMENT PROTOCOL NETWORK
Organization Name
Inventor(s)
Vikash R. Singh of San Francisco CA (US)
GEOMETRIC DEEP LEARNING AND OPTIMIZATION OF PAYMENTS ON A SECOND-LAYER PAYMENT PROTOCOL NETWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240378573 titled 'GEOMETRIC DEEP LEARNING AND OPTIMIZATION OF PAYMENTS ON A SECOND-LAYER PAYMENT PROTOCOL NETWORK
The abstract describes a method for managing cryptocurrency transactions using a second-layer payment protocol network.
- Receiving information from the second-layer payment protocol network about user node balances and channel balances.
- Obtaining additional data on balances associated with user nodes or channels.
- Generating a graph neural network (GNN) based on the obtained data.
- Calculating transaction paths between user nodes in response to requested transactions using the GNN.
- Outputting the calculated paths for transaction execution.
Potential Applications: - Efficient management of cryptocurrency transactions. - Enhanced security and privacy in transaction processing. - Facilitation of faster and more reliable transactions in cryptocurrency networks.
Problems Solved: - Streamlining transaction processing in cryptocurrency networks. - Improving the accuracy of transaction path calculations. - Enhancing the overall user experience in cryptocurrency transactions.
Benefits: - Increased efficiency in managing cryptocurrency transactions. - Improved security measures for transaction processing. - Enhanced user satisfaction with faster and more reliable transactions.
Commercial Applications: Title: "Cryptocurrency Transaction Management System" This technology can be utilized by cryptocurrency exchanges, financial institutions, and blockchain companies to optimize transaction processing and enhance user experience. It can also be integrated into existing cryptocurrency platforms to improve transaction security and efficiency.
Questions about Cryptocurrency Transaction Management System: 1. How does the use of a graph neural network improve transaction path calculations in cryptocurrency networks? - The use of a graph neural network allows for more accurate and efficient calculation of transaction paths between user nodes, leading to faster and more reliable transactions. 2. What are the potential implications of implementing this method in cryptocurrency exchanges and financial institutions? - Implementing this method can lead to increased transaction efficiency, improved security measures, and enhanced user satisfaction in cryptocurrency transactions.
Original Abstract Submitted
a method for managing a cryptocurrency transaction includes receiving information from a second-layer payment protocol network configured to process transactions between users of a cryptocurrency network. the information includes partial data indicating balances associated with user nodes in the second-layer payment protocol network or balances associated with channels between two or more of the user nodes. the method further includes obtaining additional data indicative of the balances associated with the user nodes or the balances associated with the channels, generating a graph neural network (gnn), receiving information associated with a requested transaction between user nodes in the second-layer payment protocol network, and, in response to the information associated with the requested transaction, calculating one or more paths for the transaction between the first user node and the second user node based on outputs of the gnn and outputting the one or more paths for execution of the transaction.
