Pulselight Holdings, Inc.

21ct, Inc.

Laura Hitt

Jonathan William Mugan

Jimmie Goode

Yuan Qu

Russ Gregory

Matt McClain

A recurrent neural network (RNN) method implemented on a computer system is used to produce summaries of unstructured text generated by multiple networks of individuals interacting over time by encoding the unstructured text into intermediate representations and decoding the intermediate representations into summaries of each network. Parameter data for the RNN is obtained by using multiple different versions of the same source texts to train the computer system. The method and computer system can be used to identify which of the networks match a query by determining which network generates the query with low or lowest cost.

Method and system for analyzing entities

A method for identifying a threatening network comprises an asymmetric threat signature (AT-SIG) algorithm comprising a network movement before/after algorithm that provides a graphical plot of changes in network transaction activity from before to after a specified time and further comprising one or more of: a network progression algorithm that provides a graphical plot to analyze behavior in small increments of time without specification or emphasis upon a particular time or event; a statistical network anomaly ranking algorithm that provides as output a ranked list of the networks; and an anomaly trend graphs algorithm that analyzes and visualizes the networks' anomaly scores over time. Also disclosed are an AT-SIG system and a software program product.

Method and system for identifying a threatening network

A system and method for identifying a threatening network is provided. The system comprises a network movement before/after algorithm that provides a graphical plot of changes in networks' communications activity from before to after a key event occurs, so that an analyst is able to identify anomalous behavior; a network progression algorithm that provides a graphical plot to analyze behavior in small increments of time without specification or emphasis upon a particular event, so that the analyst is able to see a trend in behavioral changes; a statistical network anomaly ranking algorithm that provides as output a ranked list of the networks; and an anomaly trend graphs algorithm that analyzes and visualizes the networks' anomaly scores over time, so that the analyst is able to see which networks are consistently suspicious, which networks accumulate more suspiciousness in response to an event, and which networks are trending toward more suspiciousness.

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Austin, TX, United States of America

Laura Hitt