| CPC G06N 20/00 (2019.01) [G06Q 20/00 (2013.01); G06Q 40/04 (2013.01)] | 15 Claims |
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1. A system configured to train and utilize a machine learning model to classify smart contracts in transactions recorded in immutable distributed electronic storage, the system comprising:
one or more processors configured by machine-readable instructions to:
obtain transaction information characterizing transactions recorded on a transaction log stored in immutable distributed electronic storage, wherein the transaction information includes first transaction information which characterizes a first transaction;
obtain classification information identifying contract classes of the transactions, individual transactions being classified in individual contract classes, wherein the classification information includes first classification information which identifies a first contract class for the first transaction;
aggregate the transaction information and the classification information into model training information;
provide the model training information to a machine learning model to train the machine learning model and generate a trained machine learning model, the trained machine learning model being trained to generate output comprising the contract classes of new transactions;
effectuate storage of the trained machine learning model;
obtain new transaction information characterizing the new transactions recorded on the transaction log, wherein the new transaction information includes first new transaction information which characterizes a second transaction and identifies a first address;
provide the new transaction information as input into the trained machine learning model, such that the first new transaction information is provided as the input into the trained machine learning model;
obtain the output from the trained machine learning model;
generate, from the output, new classification information, the new classification information including the contract classes of the new transactions, wherein the new classification information includes first new classification information which identifies the first contract class of the second transaction based on the output of the trained machine learning model;
generate user interface information defining instances of a graphical user interface through which the transactions are represented as a graph, the graphical user interface displaying, within a field of two or more dimensions, at least a graph portion of the graph, wherein graph nodes within the graph represent the addresses, and the graph nodes are presented with one or more visual characteristics assigned to the addresses based on the contract classes, such that the user interface information defines a first instance of the graphical user interface through which the second transaction is represented in a first graph portion of the graph, the first instance of the graphical user interface displaying a first graph node representing the first address;
establish one or more network connections with remotely located client computing platforms associated with users; and
effectuate communication of the user interface information to the remotely located client computing platforms associated with the users to cause the remotely located client computing platforms to present the instances of the graphical user interface, such that the user interface information defining the first instance of the graphical user interface is communicated to a first remotely located client computing platform over a first network connection to cause the first remotely located client computing platform to display the first graph portion including the first graph node presented with a first visual characteristic.
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