Invention Grant
- Patent Title: Scalable complex event processing with probabilistic machine learning models to predict subsequent geolocations
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Application No.: US15147519Application Date: 2016-05-05
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Publication No.: US10540611B2Publication Date: 2020-01-21
- Inventor: David John Reese , Annette M. Taberner-Miller , Sankalp Acharya , Lipphei Adam
- Applicant: RetailMeNot, Inc.
- Applicant Address: US TX Austin
- Assignee: RetailMeNot, Inc.
- Current Assignee: RetailMeNot, Inc.
- Current Assignee Address: US TX Austin
- Agency: Pillsbury Winthrop Shaw Pittman, LLP
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06N7/00 ; G06F16/29

Abstract:
Provided is a process, including: obtaining a set of historical geolocations; segmenting the historical geolocations into a plurality of temporal bins; determining pairwise transition probabilities between a set of geographic places based on the historical geolocations; configuring a compute cluster by assigning subsets of the transition probabilities to computing devices in the compute cluster; receiving a geolocation stream indicative of current geolocations of individuals; selecting a computing device in the compute cluster in response to determining that the computing device contain transition probabilities for the received respective geolocation; selecting transition probabilities applicable to the received respective geolocation from among the subset of transition probabilities assigned to the selected computing device; predicting a subsequent geographic place based on the selected transition probabilities.
Public/Granted literature
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