Patent ID: 8788445
Filing Date: 2014-07-22
Classification: G06N,G06Q,H04L

Abstract:
1. A system for quantifying and detecting non-normative behavior, comprising: a web-based service system to collect, profile, and assess subject object behavior, said system being configured to: (a) identify objects as discrete entities that involve interactions from different subjects and are extractable from on-line transaction events; (b) represent the similarity between two objects by coincidence in time by subject of the corresponding transaction events of those objects, aggregated across all subjects; (c) represent said objects in vector form; (d) aggregate transaction events by subject to determine the subject's affinity to each object that the subject has interacted with: (e) represent, in vector form, subjects identifying individuals involved in transactions involving said objects; (f) filter by matching a vector of a subject against subject vectors of persons of interest; (g) determine a threshold to assess said matching falls within a pre-set detection range; and (h) trigger further investigation or testing against a population of normal subjects when said matching falls within said pre-set detection range, whereby said system quantifies and detects a non-normative behavior of said subject by applying a suspicion threshold, and further whereby the subject vector and the object vector each have a respective number of dimensions; and further whereby the predicted similarity of one object to another object is calculated by matching their object vectors; and further whereby the system is configured to generate the object vectors by producing object vectors having respective initial dimensions, to determine predicted similarity values based on the initial object vectors, and to calculate a cost function that measures the difference between the predicted similarity values and the said similarity between two objects by coincidence in time by subject; and further whereby the system iteratively increases the dimensions of the generated object vectors, and recalculates the cost function based on the differences between the predicted similarity values and actual similarity values, until the cost function reaches a predetermined value, and wherein the actual similarity values are based on said transaction events; and further whereby the system is configured to generate the subject vectors from the said object vectors and said affinities derived from said transaction events.